Hyper Spectral Imaging

This paper studies the detection of vegetation stress in orchards via remote sensing. During previous research, it was shown that stress can be detected reliably on hyperspectral reflectances of the fresh leaves, using a generic wavelet based hyperspectral classification. In this work, we demonstrate the capability to detect stress from airborne/spaceborne hyperspectral sensors by upscaling the leaf reflectances to top of atmosphere (TOA) radiances. Several data sets are generated, measuring the foliar reflectance with a portable field spectroradiometer, covering different time periods, fruit variants and stress types. We concentrated on the Jonagold and Golden Delicious apple trees, induced with mildew and nitrogen deficiency. First, a directional homogeneous canopy reflectance model (ACRM) is applied on these data sets for simulating top of canopy (TOC) spectra. Then, the TOC level is further upscaled to TOA, using the atmospheric radiative transfer model MODTRAN4. To simulate hyperspectral imagery acquired with real airborne/spaceborne sensors, the spectrum is further filtered and subsampled to the available resolution. Using these simulated upscaled TOC and TOA spectra in classification, we will demonstrate that there is still a differentiation possible between stressed and non-stressed trees. Furthermore, results show it is possible to train a classifier with simulated TOA data, to make a classification of real hyperspectral imagery over the orchard.

The German Aerospace Center DLR -namely the Earth Observation Center EOC and the German Space Operations Center GSOCis responsible for the establishment of the ground segment of the future German hyperspectral satellite mission EnMAP (Environmental Mapping and Analysis Program). The Earth Observation Center has long lasting experiences with air-and spaceborne acquisition, processing, and analysis of hyperspectral image data. In the first part of this paper, an overview of the radiometric in-flight calibration concept including dark value measurements, deep space measurements, internal lamps measurements and sun measurements is presented. Complemented by pre-launch calibration and characterization these analyses will deliver a detailed and quantitative assessment of possible changes of spectral and radiometric characteristics of the hyperspectral instrument, e.g. due to degradation of single elements. A geometric accuracy of 100 m, which will be improved to 30 m with respect to a used reference image, if it exists, will be achieved by ground processing. Therfore, and for the required co-registration accuracy between SWIR and VNIR channels, additional to the radiometric calibration, also a geometric calibration is necessary. In the second part of this paper, the concept of the geometric calibration is presented in detail. The geometric processing of EnMAP scenes will be based on laboratory calibration results. During repeated passes over selected calibration areas images will be acquired. The update of geometric camera model parameters will be done by an adjustment using ground control points, which will be extracted by automatic image matching. In the adjustment, the improvements of the attitude angles (boresight angles), the improvements of the interior orientation (view vector) and the improvements of the position data are estimated. In this paper, the improvement of the boresight angles is presented in detail as an example. The other values and combinations follow the same rules. The geometric calibration will mainly be executed during the commissioning phase, later in the mission it is only executed if required, i.e. if the geometric accuracy of the produced images is close to or exceeds the requirements of 100 m or 30 m respectively, whereas the radiometric calibration will be executed periodically during the mission with a higher frequency during commissioning phase.

We address the problem of complexity reduction in hyperspectral image unmixing. When the hyperspectral images are highly resoluted, we propose to select a limited number of pixels, therefore reducing dramatically the size of the data. Then, the related mixtures are used as inputs to a positive source separation algorithm. Our pixel selection procedure is based on a convex cone analysis of the data mixtures; indeed, positive mixtures of sources are embedded in a convex cone whose boundary contains complete available information regarding the sources. We search for the least number of mixtures embedding the convex cone and then store the corresponding pixel indices as the selected pixels. We apply this method to the analysis of hyperspectral images of bacterial cells obtained on a confocal microscope. The bacterial cells, acting as whole-cell biosensors, display great potential as living transducers in sensing applications.

This poster aims to discuss the limitations and benefits of different imaging techniques when capturing fine inscriptions on
objects made of bone, wood and leather, with the explicit goal of producing models suitable for scientific study. The case
study is conducted on a corpus of approximately 60 runic inscriptions from medieval Oslo, dating between the 11th and 13th
centuries and will be published through the museums existing online web interfaces, Unimusportalen, and BItFROST.

This study addresses conventional image processing shortcomings via one cohesive system integrating SRGAN, DeepFillv2, and CycleGAN (Generative Adversarial Networks). Image enhancement along with clever completion also versatile style transformations including colorization and decolorization are accessible through an intuitive Streamlit web interface. SRGAN is known to excel at improving image resolution and DeepFillv2 reconstructs damaged areas skillfully with full contextual awareness. CycleGAN eases style changes between domains without need of matched datasets. The system performs during real-time, features dependability, and outputs superior visuals. SSIM evaluation validates its utility. It makes this an important instrument for artists, designers, and photographers.

Independent component analysis (ICA) is a very popular method that has shown success in blind source separation, feature extraction and unsupervised recognition. In recent years ICA has been largely studied by researchers from the signal processing community. This paper addresses a more in-depth study on the use of this method, applied to hyperspectral images used for remote sensing purposes. In a first part, source separation is addressed. Since the independence of sources is usually not verified in hyperspectral real data images, ICA, if used alone, is not a suitable tool to unmix sources. We propose a hierarchical approximation for the use of ICA as a pre-processing step for a Bayesian Positive Source Separation method. In a second part, the use of ICA for dimensionality reduction is studied in the frame of hyperspectral data classification. Experimental results show the effectiveness of ICA when used for hyperspectral image pre-processing for the two considered applications.

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

We developed a forest fire sensor web concept with a UAVSAR-based smart sensor and onboard automated response capability that will allow us to monitor fire progression based on coarse initial information provided by an external source. This autonomous disturbance detection and monitoring system combines the unique capabilities of imaging radar with high throughput onboard processing technology and onboard automated response capability based on specific science algorithms. In this forest fire sensor web scenario, a fire is initially located by MODIS/RapidFire or a ground-based fire observer. This information is transmitted to the UAVSAR onboard automated response system (CASPER). CASPER generates a flight plan to cover the alerted fire area and executes the flight plan. The onboard processor generates the fuel load map from raw radar data, used with wind and elevation information, predicts the likely fire progression. CASPER then autonomously alters the flight plan to track the fire progression, providing this information to the fire fighting team on the ground. We can also relay the precise fire location to other remote sensing assets with autonomous response capability such as Earth Observation-1 (EO-1)'s hyper-spectral imager to acquire the fire data.

We propose a feature selection-based approach for improving classification performance of a two stage classification system in contexts where a high number of features is involved. A problem with a set of N classes is subdivided into a set of N two class problems. In each problem, a GAbased feature selection algorithm is used for finding the best subset of features. These subsets are then used for training N classifiers. In the classification phase, unknown samples are given in input to each of the trained classifiers by using the corresponding subspace. In case of conflicting responses, the sample is sent to a suitably trained supplementary classifier. The proposed approach has been tested on a real world dataset containing hyper-spectral image data. The results favourably compare with those obtained by other methods on the same data.

We propose a feature selection-based approach for improving classification performance of a two stage classification system in contexts where a high number of features is involved. A problem with a set of N classes is subdivided into a set of N two class problems. In each problem, a GAbased feature selection algorithm is used for finding the best subset of features. These subsets are then used for training N classifiers. In the classification phase, unknown samples are given in input to each of the trained classifiers by using the corresponding subspace. In case of conflicting responses, the sample is sent to a suitably trained supplementary classifier. The proposed approach has been tested on a real world dataset containing hyper-spectral image data. The results favourably compare with those obtained by other methods on the same data.

BepiColombo is the cornerstone n.5 of ESA, and it will be launched on August 2013. It is composed by two modules: the Mercury Planetary Orbiter (MPO) and the Mercury Magnetospheric Orbiter (MMO), and with a suite of instruments which shall allow to perform observations with a level of details considerably improved respect to the NASAs Messenger mission launched in 2004. The SIMBIO-SYS (Spectrometers and Imagers for MPO BepiColombo Integrated Observatory SYStem) instrument is a system integrating a STereoscopic imaging Channel (STC), a High spatial Resolution Imaging Channel (HRIC) and a Visual and Infrared Hyper-spectral Imager channel (VIHI). SIMBIO-SYS has been selected by ESA in the payload of the MPO. The main scientific objective of STC is the global mapping of the entire surface of Mercury in 3D and colors with a maximum spatial resolution of 50 m per pixel. It will allow to generate the Digital Terrain Model (DTM) of the entire surface improving the interpretation of morphological features at different scales and topographic relationships.

The overarching goal of this research was to develop methods and protocols for mapping irrigated areas using a Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m time series, to generate irrigated area statistics, and to compare these with ground-and census-based statistics. The primary mega-file data-cube (MFDC), comparable to a hyper-spectral data cube, used in this study consisted of 952 bands of data in a single file that were derived from MODIS 500 m, 7-band reflectance data acquired every 8days during 2001-2003. The methods consisted of (a) segmenting the 952-band MFDC based not only on elevation-precipitation-temperature zones but on major and minor irrigated command area boundaries obtained from India's Central Board of Irrigation and Power (CBIP), (b) developing a large ideal spectral data bank (ISDB) of irrigated areas for India, (c) adopting quantitative spectral matching techniques (SMTs) such as the spectral correlation similarity (SCS) R 2 -value, (d) establishing a comprehensive set of protocols for class identification and labeling, and (e) comparing the results with the National Census data of India and field-plot data gathered during this project for determining accuracies, uncertainties and errors. The study produced irrigated area maps and statistics of India at the national and the subnational (e.g., state, district) levels based on MODIS data from 2001-2003. The Total Area Available for Irrigation (TAAI) and Annualized Irrigated Areas (AIAs) were 113 and 147 million hectares (MHa), respectively. The TAAI does not consider the intensity of irrigation, and its nearest equivalent is the net irrigated areas in the Indian National Statistics. The AIA considers intensity of irrigation and is the equivalent of ''irrigated potential utilized (IPU)'' reported by India's Ministry of Water Resources (MoWR). The field-plot data collected during this project showed that the accuracy of TAAI classes was 88% with a 12% error of omission and 32% of error of commission. Comparisons between the AIA and IPU produced an R 2 -value of 0.84. However, AIA was consistently higher than IPU. The causes for differences were both in traditional approaches and remote sensing. The causes of uncertainties unique to traditional approaches were (a) inadequate accounting of minor irrigation (groundwater, small reservoirs and tanks), (b) unwillingness to share irrigated area statistics by the individual Indian states because of their stakes, (c) absence of comprehensive statistical analyses of reported data, and (d) subjectivity involved in observationbased data collection process. The causes of uncertainties unique to remote sensing approaches were (a) irrigated area fraction estimate and related sub-pixel area computations and (b) resolution of the imagery. The causes of uncertainties common in both traditional and remote sensing approaches were definitions and methodological issues.

Hyperspectral imaging captures a broad spectrum of wavelengths, revealing detailed spectral signatures essential for precise material identification. The current research addresses the challenges of high dimensionality and computational inefficiency in hyperspectral data classification, which often impede the effective application of conventional machine learning techniques. By introducing a novel methodology combining Linear Discriminant Analysis (LDA) for dimensionality reduction and the Scaled Extra Tree Classifier for robust classification, this study innovatively enhances the accuracy and efficiency of hyperspectral image analysis. The proposed method initially employs LDA to reduce the data's dimensionality, thereby mitigating the curse of dimensionality and improving computational speed. Following dimensionality reduction, the Scaled Extra Tree Classifier, a modification of the standard Extra Trees method, is utilized to handle the classification. This classifier benefits from an integrated preprocessing step that normalizes the feature space, ensuring that each spectral band contributes equally to the classification process. The novelty lies in this integration, optimizing the classifier's performance across various spectral signatures. Experimental results demonstrate significant improvements in classification accuracy at 80%. The classifier achieves a marked increase in precision, recall, and F1-scores across multiple classes compared to traditional methods, with notable resilience against overfitting.

The Advanced Baseline Imager (ABI) on GOES-R will help NOAA's objective of engaging and educating the public on environmental issues by providing near real-time imagery of the earth-atmosphere system. True color satellite images are beneficial to the public, as well as to scientists, who use these images as an important "decision aid" and visualization tool. Unfortunately, ABI only has two visible bands (cyan and red) and does not directly produce the three bands (blue, green, and red) used to create true color imagery. We have developed an algorithm that will produce quantitative true color imagery from ABI. Our algorithm estimates the three tristimulus values of the international standard CIE 1931 XYZ colorspace for each pixel of the ABI image, and thus is compatible with a wide range of software packages and hardware devices. Our algorithm is based on a non-linear statistical regression framework that incorporate both classification and local multispectral regression using training data. We have used training data from the hyper-spectral imager Hyperion. Our algorithm to produce true color images from the ABI is not specific to ABI and may be applicable to other satellites which, like the ABI, do not have the ability to directly produce RGB imagery.

Water quality monitoring through remote sensing with UAVs is best conducted using multispectral sensors; however, these sensors are expensive. We aimed to predict multispectral bands from a low-cost sensor (R, G, B bands) using artificial neural networks (ANN). We studied a lake located on the campus of Unisinos University, Brazil, using a low-cost sensor mounted on a UAV. Simultaneously, we collected water samples during the UAV flight to determine total suspended solids (TSS) and dissolved organic matter (DOM). We correlated the three bands predicted with TSS and DOM. The results show that the ANN validation process predicted the three bands of the multispectral sensor using the three bands of the low-cost sensor with a low average error of 19%. The correlations with TSS and DOM resulted in R² values of greater than 0.60, consistent with literature values.

Water quality monitoring through remote sensing with UAVs is best conducted using multispectral sensors; however, these sensors are expensive. We aimed to predict multispectral bands from a low-cost sensor (R, G, B bands) using artificial neural networks (ANN). We studied a lake located on the campus of Unisinos University, Brazil, using a low-cost sensor mounted on a UAV. Simultaneously, we collected water samples during the UAV flight to determine total suspended solids (TSS) and dissolved organic matter (DOM). We correlated the three bands predicted with TSS and DOM. The results show that the ANN validation process predicted the three bands of the multispectral sensor using the three bands of the low-cost sensor with a low average error of 19%. The correlations with TSS and DOM resulted in R² values of greater than 0.60, consistent with literature values.

Gradient extraction is important for a lot of metrological applications such as Control Quality by Vision. In this work, we propose a full-vector gradient for multi-spectral sensors. The full-vector gradient extends Di Zenzo expression to take into account the non-orthogonality of the acquisition channels thanks to a Gram matrix. This expression is generic and independent from channel count. Results are provided for a color and a multi-spectral snapshot sensor. Then, we show the accuracy improvement of the gradient calculation by creating a dedicated objective test and from real images.

Gradient extraction is important for a lot of metrological applications such as Control Quality by Vision. In this work, we propose a full-vector gradient for multi-spectral sensors. The full-vector gradient extends Di Zenzo expression to take into account the non-orthogonality of the acquisition channels thanks to a Gram matrix. This expression is generic and independent from channel count. Results are provided for a color and a multi-spectral snapshot sensor. Then, we show the accuracy improvement of the gradient calculation by creating a dedicated objective test and from real images.

Gradient extraction is important for a lot of metrological applications such as Control Quality by Vision. In this work, we propose a full-vector gradient for multi-spectral sensors. The full-vector gradient extends Di Zenzo expression to take into account the non-orthogonality of the acquisition channels thanks to a Gram matrix. This expression is generic and independent from channel count. Results are provided for a color and a multi-spectral snapshot sensor. Then, we show the accuracy improvement of the gradient calculation by creating a dedicated objective test and from real images.

Skin blood haemoglobin saturation (b) can be estimated with hyperspectral imaging using the wavelength () range of 450-700 nm where haemoglobin absorption displays distinct spectral characteristics. Depending on the image size and photon transport algorithm, computations may be demanding. Therefore, this work aims to evaluate subsets with a reduced number of wavelengths for b estimation. White Monte Carlo simulations are performed using a two-layered tissue model with discrete values for epidermal thickness (epi) and the reduced scattering coefficient (s ′), mimicking an imaging setup. A detected intensity look-up table is calculated for a range of model parameter values relevant to human skin, adding absorption effects in the post-processing. Skin model parameters, including absorbers, are; s ′ (), epi , haemoglobin saturation (b), tissue fraction blood (b) and tissue fraction melanin (mel). The skin model paired with the look-up table allow spectra to be calculated swiftly. Three inverse models with varying number of free parameters are evaluated: A(b , b), B(b , b , mel) and C(all parameters free). Fourteen wavelength candidates are selected by analysing the maximal spectral sensitivity to b and minimizing the sensitivity to b. All possible combinations of these candidates with three, four and 14 wavelengths, as well as the full spectral range, are evaluated for estimating b for 1000 randomly generated evaluation spectra. The results show that the simplified models A and B estimated b accurately using four wavelengths (mean error 2.2% for model B). If the number of wavelengths increased, the model complexity needed to be increased to avoid poor estimations.

ESA currently builds the airborne hyper-spectral push broom imaging spectrometer APEX operating in the spectral range from 380 to 2500 nm. In the scope of the APEX project a large variety of characterization measurements will be performed for verification of the instrument performance. Later, during the operational phases, regular instrument checks will be executed, i.e., on-board characterization, frequent laboratory characterization and vicarious calibration. The APEX instrument will achieve its challenging measurement accuracy by regular calibration of the instrument between flight cycles and secondly guarantee an excellent performance by using its "In-Flight Characterisation unit" (IFC) during flight operation. For on-ground characterisation, a dedicated characterisation and calibration facility is in preparation to enable a comprehensive and accurate calibration of the instrument. The so-called "Calibration Home Base" (CHB) is located at DLR Oberpfaffenhofen and will be operational from 2006. The CHB provides all hard-and software tools required for radiometric, spectral and geometric on-ground characterisation and calibration of the instrument and its internal references and to perform measurements on polarisation and stray light sensitivity and comprises a test bed and the provision of the infrastructure. In view of the high relevance to scientific objectives of its Living Planet Programme, ESA decided to contribute to the realisation of APEX by providing and funding among others the CHB. In this paper the calibration equipment and concept is outlined.

We propose an extension of the self-organizing map for supervised fuzzy classification learning, whereby uncertain (fuzzy) class information is also allowed for training data. The method is able to detect class similarities, which can be used for data vizualization. Applying a special functional metric, derived from of the L_p norms, we show the application of the method for classification and visualization of hyper-spectral data in satellite image remote sensing image analysis.

Straightforward mapping of detailed heathland habitat patches and their quality using remote sensing is hampered by (1) the intrinsic property of a high heterogeneity in habitat species composition (i.e. high intra-variability), and (2) the occurrence of the same species in multiple habitat types (i.e. low inter-variability). Mapping accuracy of detailed habitat objects can however be improved by using an advanced approach that specifically takes into account and exploits these inherent patch characteristics. To demonstrate the idea, we developed and applied a multi-step mapping framework on a protected semi-natural heathland area in the north of Belgium. The method consecutively consists of (1) a 4-level hierarchical land cover classification of hyperspectral airborne AHS image data, and (2) a kernel-based structural re-classification algorithm in combination with habitat patch object composition definitions. Detailed land cover composition data were collected in 1325 field plots. Multi-variate analysis (Ward's clustering; TWINSPAN) of these data led to the design of meaningful land cover classes in a dedicated classification scheme. Subsequently, the data were used as reference for the classification of hyperspectral AHS image data. Linear Discriminant Analysis in combination with Sequential-Floating-Forward-Selection (SFFS-LDA) was applied to classify the hyperspectral images. Classification accuracies of these maps are in the order of 74-93% (Kappa= 0.81-0.92) depending on the classification detail. To subsequently obtain habitat patch (object) maps, the land cover classifications were used as input for a kernel-based spatial re-classification process, in combination with a rule-set that relates specific Natura 2000 habitats with a composition range of the land cover classes. The resulting habitat patch maps illustrate the methodology's potential for detailed heathland habitat characterization using hyperspectral image data, and hence contribute to the improved mapping and understanding of heathland habitat, essential for the EU member states reporting obligations under the Habitats Directive.

A new method for supervised hyperspectral data classification is proposed. In particular, the notion of Stochastic Minimum Spanning Forests (MSFs) is introduced. For a given hyperspectral image, a pixelwise classification is first performed. From this classification map, M marker maps are generated by randomly selecting pixels and labeling them as markers for the construction of MSFs. The next step consists in building an MSF from each of the M marker maps. Finally, all the M realizations are aggregated with a maximum vote decision rule, resulting in a final classification map. The experimental results presented on an AVIRIS image of the vegetation area show that the proposed approach yields accurate classification maps, and thus is attractive for hyperspectral data analysis.

This exciting series publishes both monographs and edited thematic collections in the broad areas of cultural heritage, digital humanities, collecting and collections, public history and allied areas of applied humanities. In the spirit of our mission to take a stand for the humanities, this series illustrates humanities research keeping pace with technological innovation, globalization, and democratization. We value a variety of established, new, and diverse voices and topics in humanities research and this series provides a platform for publishing the results of cuttingedge projects within these fields. and Space in Cultural Heritage (www.cosch.info), supported by the European Cooperation in Science and Technology (COST, www.cost.eu). COST is a funding agency for research and innovation networks. COST Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation.

This exciting series publishes both monographs and edited thematic collections in the broad areas of cultural heritage, digital humanities, collecting and collections, public history and allied areas of applied humanities. In the spirit of our mission to take a stand for the humanities, this series illustrates humanities research keeping pace with technological innovation, globalization, and democratization. We value a variety of established, new, and diverse voices and topics in humanities research and this series provides a platform for publishing the results of cuttingedge projects within these fields. and Space in Cultural Heritage (www.cosch.info), supported by the European Cooperation in Science and Technology (COST, www.cost.eu). COST is a funding agency for research and innovation networks. COST Actions help connect research initiatives across Europe and enable scientists to grow their ideas by sharing them with their peers. This boosts their research, career and innovation.

The next generation of MURATest will be able to make a hyper spectral analysis to get a high definition image with adjustable wavelength resolution. This paper introduces the principle of the new system, based on an imaging polarizing interferometer. The phase shift is realized by two symmetrical Wollaston prisms. The system produces an interference pattern directly on the CCD camera. The phase shift is linearly changed for all pixels using a mechanical translation.

Age Related Macular Degeneration (ARMD) is a type of eye disease which normally have an effect on the central vision of a person. This Disease might sometimes lead to permanent vision loss for some people. It affects the people over the age of 50. So, basically there are 2 different types of ARMD i.e., Dry and Wet. Dry ARMD will generate a tiny amount of protein deposits called drusen, whereas Wet ARMD occurs whenever any abnormal blood vessel is developed under the retina, so sometimes this blood vessels might leak blood fluid, this type of ARMD is very severe and can even lead to permanent central vision loss. Therefore, it is necessary for early detection of the disease. Generative Data Augmentation for ARMD Classification is deep learning based which uses Convolutional Neural Network (CNN) model for generating images to accurately identify the disease. Deep Learning Diagnostic models require expertly graded images from extensive data sets obtained in large scale clinical trials which may not exist. Therefore, (Generative Adversarial Networks) GAN-based generative data augmentation method called Style GAN is used for generating the images. Generative deep learning techniques is used to synthesize new large datasets of artificial retinal images from different stages of ARMD using the images from the already existing datasets. The performance of ARMD diagnostic DCNNs will be trained on the combination of both real and synthetic datasets. Images obtained by using GAN appear to be realistic, and increase the accuracy of the model. It then continues with classifying the retinal images into one of the three classes i.e., dry, wet or normal using CNN model. It also compares the accuracy against the model with traditional augmentation techniques, towards improving the performance of real-world ARMD classification tasks.

Significantly enhanced absorption at the defect mode can be obtained at surfacenormal direction in a dielectric single-defect photonic-crystal-slab, with an absorption enhancement factor greater than 4,000. Complete absorption suppression within the photonic bandgap region can also be observed in defect-free photonic crystal cavities. High spectral selectivity and tunability is feasible with defect mode engineering, making photonic crystal defect cavities a promising nanophotonic platform for the spectrally selective infrared sensing and hyper-spectral imaging, with the incorporation of quantum well or quantum dot infrared photodetector heterostructures.

Biodiversity loss remains a global challenge despite international commitment to the United Nations Convention on Biodiversity. Biodiversity monitoring methods are often limited in their geographical coverage or thematic content. Furthermore, remote sensing-based integrated monitoring methods mostly attempt to determine species diversity from habitat heterogeneity somewhat reflected in the spectral diversity of the image used. Up to date, there has been no standardized method for monitoring biodiversity against the backdrop of ecosystem or environmental pressures. This study presents a new method for monitoring the impact of oil pollution an environmental pressure on biodiversity at regional scale and presents a case study in the Niger delta region of Nigeria. It integrates satellite remote sensing and field data to develop a set of spectral metrics for biodiversity monitoring. Using vascular plants of various lifeforms observed on polluted and unpolluted (control) locations, as surrogates for biodiversity, the normalized difference vegetation vigour index (NDVVI) variants were estimated from Hyperion wavelengths sensitive to petroleum hydrocarbons and evaluated for potential use in biodiversity monitoring schemes. The NDVVI ranges from 0 to 1 and stems from the presupposition that increasing chlorophyll absorption in the green vegetation can be used as a predictor to model vascular plant species diversity. The performances of NDVVI variants were compared to traditional narrowband vegetation indices (NBVIs). The results show strong links between vascular plant species diversity and primary productivity of vegetation quantified by the chlorophyll content, vegetation vigour and abundance. An NDVVI-based model gave much more accurate predictions of species diversity than traditional NBVIs (R-squared and prediction square error (PSE) respectively for Shannon's diversity = 0.54 and 0.69 for NDVVIs and 0.14 and 0.9 for NBVIs). We conclude that NDVVI is a superior remote sensing index for monitoring biodiversity indicators in oil-polluted areas than traditional NBVIs.

Hyperspectral images from three sensors were compared for their ability to discriminate and map selected symptoms of salinity in a dryland agricultural area in southern Australia. The symptoms mapped are widespread in this environment: the perennial halophytic shrub samphire (Halosarcia pergranulata), a salt tolerant grass, sea barley grass (Hordeum marinum) and salt encrusted pans. Airborne HyMap and satellite Hyperion imagery were acquired in the late summer dry season to maximise soil and perennial vegetation discrimination, while airborne CASI imagery was acquired in late spring to capture senescence of the annual sea barley grass. Image spectra were used to map surface salinity symptoms using partial spectral unmixing techniques. Saltpans were discriiiminated using the gypsum 1750 nm absorption feature, while full-wavelength image spectra were necessary to map the halophytic plants. Salinity symptom maps were validated at over 100 random field sites and compared with independen...

Two issues of great interest in the field of lasers in artwork conservation are the so-called yellowing effect and the discoloration of pigments. We have viewed these issues from a comprehensive point of view, considering all our present experimental results as well as ongoing modeling and theoretical calculations. The first concern to be discussed is the yellowing effect in laser cleaning of marble or stone artifacts. Although, in most cases, a yellowish layer exists underneath the black encrustation, the so-called 'patina', it has become clear that there are situations where yellowing cannot be attributed to an existing layer. In the present study, a light scattering model that may account for the yellowing is presented. This model considers a thin absorbent layer and the surface roughness and/or created voids and accounts for the reflectance spectra measured by (i) hyper-spectral imaging and (ii) integrating sphere. Additional experimental data, such as the absence of yellowing when the third harmonic of a Q-swihed Nd:YAG laser is used, support this model. A thorough understanding of the quantitative characteristics of pigment discoloration, on the other hand, has been attempted by means of X-ray diffraction and theoretical studies. The model developed suggests a nucleation process for cinnabar resulting in a structural modification within the volume of a pigment's crystal or particle close to the 'ablation front', which extends for a few nanometers from its surface.

Sunglint, also known as specular reflection of light from water surfaces into sensor optics, is a component of sensor-incoming radiance that represents a degrading factor on the spectral characterization of water bodies. Airborne remote sensors are suitable to evaluate and monitor freshwater ecosystems, unfortunately those sensors can be heavily affected by sunglint, due to their high spatial resolution. Although, sunglint effects can be minimized in airborne remote sensing by optimizing the viewing geometry, those conditions cannot always be applied, due to the size and spatial pattern of inland water bodies, so methods to identify and correct for extraneous radiance contributions are essential. The present study reviews current techniques to remove sunglint radiance component from high spatial resolution images and tests a deglint algorithm in a set of airborne hyperspectral images acquired under different viewing geometry. The results demonstrate the sunglint effects damaging the images, directly influencing its visual aspects, the magnitude of the image spectra under glint conditions and masking the correlations between the optically active substances and the spectral data. The algorithm applied showed good results removing the sunglint in the images, providing a viable solution to mitigate those effects in higher resolution hyperspectral images of optically complex waters, acquired by airborne sensors.

The use of hyper-spectral imaging technique to reveal concealed layers: a key question for the study of paintings Hyper-spectral imaging, based on a high performance scanner operating at both high spectral and spatial resolution in the visible (Vis) and near-infrared (NIR) regions, has been applied as an in situ techniq ue for the study and digital documentation of cultural heritage. When paintings are concerned, hyper-spectral imaging application in the NIR range is particularly important to provide essentia l information about the artworks' construction to museums professionals, conservato rs, and art historians. Paintings are frequently composed by different complex layers and those below the surface are usually invisible to the naked eye. Among these layers are the underdrawings that were generally created by the artists with different techniques and materials, typically charcoal and chalk, metalpoints, and inks. Since some of them absorb the NIR radiation it is possible to unveil and study what may be present under the visible co loured layers. However, the visualisation of these drawings may depend on the transparency of the coloured paint layers. Moreover a high spatial resolution is generally required in order to allow the di scrimination between the mul tiple lines of the underdrawings. As such, the possibility to reconstruct highly resolved images at different wavelengths, which allows to go further into the different layers and obtain a higher degree of visualisation, is of great advantage. In th is context, understanding which is the minimum spatial and spectral resolution needed for the spectral imaging systems to provide useful and accurate in-depth information is one of the questions addressed by COSCH Working Group l (WG I) and which will be discussed in the present work. Within an interdisciplinary co llaboration between the WG 1 community, museum professionals, conservation scientists, and technology specialists, a painted wood pane l reconstructed according to a medieval technique described by Cennino Cennini was prepared and analysed with a push-broom hyper-spectral imaging scanner in the NIR range. On top of the panel's preparation layer, various lines and drawings were created with different materials (lead-based metalpoint, lead-and tin-based metalpoint, graphite, charcoal, and watercolour), wh ich were then hidden by the coloured paint layers applied above. These underdrawings were used to assess the actual performances of the hyper-spectral imaging system and to evaluate its ability to discriminate between the patterns of lines underneath layers of variable thickness and diverse chemical composition.

This paper focuses on comparing three basis-vector selection techniques as applied to target detection in hyperspectral imagery. The basis-vector selection methods tested were the singular value decomposition (SVD), pixel purity index (PPI), and a newly developed approach called the maximum distance (MaxD) method. Target spaces were created using an illumination invariant technique, while the background space was generated from AVIRIS hyperspectral imagery. All three selection techniques were applied (in various combinations) to target as well as background spaces so as to generate dimensionally-reduced subspaces. Both target and background subspaces were described by linear subspace models (i.e., structured models). Generated basis vectors were then implemented in a generalized likelihood ratio (GLR) detector. False alarm rates (FAR) were tabulated along with a new summary metric called the average false alarm rate (AFAR). Some additional summary metrics are also introduced. Impact of the number of basis vectors in the target and background subspaces on detector performance was also investigated. For the given AVIRIS data set, the MaxD method as applied to the background subspace outperformed the other two methods tested (SVD and PPI).

We present a novel Hyper-Spectral Imaging (HySI) System capable of acquiring and real time displaying of 5nm specrtal images, with 2nm tuning step, in the range of 400nm-1000nm. Synchronized spectral scanning and image storing enables the collection of a stack of calibrated spectral images, from which a fully resolved spectrum per image pixel can be calculated and displayed. We also present results from a pilot use of the developed HySI system as a research tool, in an attempt to develop novel, non-invasive diagnostic ...

Image fusion aims at the exploitation of the information conveyed by data acquired by different imaging sensors. A notable application is merging images acquired from space by panchromatic and multi-or hyper-spectral sensors that exhibit complementary spatial and spectral resolution. Multiresolution analysis has been recognized efficient for image fusion. The Generalized Laplacian Pyramid (GLP), in particular, has been proven as the most efficient scheme due to its capability of managing images whose scale ratios are fractional numbers (non-dyadic data) and to its simple and easy implementation. Data merge based on multiresolution analysis, however, requires the definition of a model establishing how the missing spatial details to be injected into the multispectral bands are extracted from the panchromatic image. The model can be global over the whole image or depend on the local space-spectral context. This paper reports results on the fusion of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. Each of the five thermal infrared (TIR) images (90m) is merged with the most correlated visible-near infrared (VNIR) image (15m). Due to the 6:1 scale ratio, the GLP has been utilized. The injection of spatial details has been ruled by means of the Spectral Distortion Minimizing (SDM) model that minimises the spectral distortion between the resampled and fused images. Notwithstanding the lack of a spectral overlap between the VNIR and the TIR bands, experimental results show that the fused images keep their spectral characteristics while the spatial resolution is enhanced.

Sagem is presently working on a new project for the Japanese HISUI instrument made from a Hyper Spectral Sensor and a Multi Spectral Sensor, both including a Three Mirror Anastigmat (TMA) main optics. Mirrors are made from Zerodur from Schott but also from NTSIC, the New Technology Silicon Carbide developed in Japan. This report is also the opportunity to show to the community Sagem recent progress in precision TMA optics polishing and alignment.

Recently, most critical challenge tasks in remote sensing applications are the segmentation and classification of hyperspectral images (HSI). Data dependency and relation among the frequency bands are the major drawbacks in the traditional HSI segmentation and classification techniques for large-size images. To segment and classify the regions in HSI, the clear description of the edge information is necessary. The presence of noise needs to be removed prior to the clear edge information extraction. To alleviate such issues, the new way of pattern extraction is proposed in this paper. Initially, the fuzzy-based adaptive median filtering removes the noise present in the image which is the prior step for clear information extraction. The integration of circular local binary pattern with the sorted local horizontal vector (CHV) patterns extracts the clear edge information necessary for classification. The intensity values from the color-based feature extraction and the CHV pattern are used to construct the relevance vector machine kernel with the limits. The comparative analysis between the proposed CHVbased pattern extraction grid with the existing techniques regarding the various metrics such as accuracy, sensitivity, specificity, rate variations and coefficient variations assures the effectiveness of proposed work in remote sensing applications. Communicated by M. Anisetti.

This paper 1,2 presents the preliminary results obtained within a research project aimed at the development of a remote sensing system for forest fires monitoring in missions of compact airborne platforms. The core of the system is an integrated, multi-/hyper-spectral suite of electro-optical sensors. They were selected to get enhanced ability in forest fire detection and monitoring. The system is completed by a dedicated on-board computer for sensor control. It is capable of autonomous operation and it is also in charge of data exchange with the on-board navigation and flight control system. Two different configurations are described, relevant to different aircrafts: a mini UAV and a certified, two-seat light aircraft. Results of the first flight experiments are presented, that highlight performance achievable by the system.

The German Aerospace Center DLR-namely the Earth Observation Center EOC and the German Space Operations Center GSOCis responsible for the establishment of the ground segment of the future German hyperspectral satellite mission EnMAP (Environmental Mapping and Analysis Program). The Earth Observation Center has long lasting experiences with air-and spaceborne acquisition, processing, and analysis of hyperspectral image data. In the first part of this paper, an overview of the radiometric in-flight calibration concept including dark value measurements, deep space measurements, internal lamps measurements and sun measurements is presented. Complemented by pre-launch calibration and characterization these analyses will deliver a detailed and quantitative assessment of possible changes of spectral and radiometric characteristics of the hyperspectral instrument, e.g. due to degradation of single elements. A geometric accuracy of 100 m, which will be improved to 30 m with respect to a used reference image, if it exists, will be achieved by ground processing. Therfore, and for the required co-registration accuracy between SWIR and VNIR channels, additional to the radiometric calibration, also a geometric calibration is necessary. In the second part of this paper, the concept of the geometric calibration is presented in detail. The geometric processing of EnMAP scenes will be based on laboratory calibration results. During repeated passes over selected calibration areas images will be acquired. The update of geometric camera model parameters will be done by an adjustment using ground control points, which will be extracted by automatic image matching. In the adjustment, the improvements of the attitude angles (boresight angles), the improvements of the interior orientation (view vector) and the improvements of the position data are estimated. In this paper, the improvement of the boresight angles is presented in detail as an example. The other values and combinations follow the same rules. The geometric calibration will mainly be executed during the commissioning phase, later in the mission it is only executed if required, i.e. if the geometric accuracy of the produced images is close to or exceeds the requirements of 100 m or 30 m respectively, whereas the radiometric calibration will be executed periodically during the mission with a higher frequency during commissioning phase.

The focus of this work is on developing a new hierarchical hybrid Support Vector Machine (SVM) method to address the problems of classification of multi or hyper spectral remotely sensed images and provide a working technique that increases the classification accuracy while lowering the computational cost and complexity of the process. The paper presents issues in analyzing large multi/hyper spectral image data sets for dimensionality reduction, coping with intra pixel spectral variations, and selection of a flexible classifier with robust learning process. Experiments conducted revealed that a computation-ally cheap algorithm that uses Hamming distance between the pixel vectors of different bands to eliminate redundant bands was quite effective in helping reduce the dimensionality. The paper also presents the concept of extended mathematical morphological profiles for segregating the input pixel vectors into pure or mixed categories which will enable further computational cost reductions. The proposed method's overall classification accuracy is tested with IRS data sets and the Airborne Visible Infrared Imaging Spectroradiometer Indian Pines hyperspectral benchmark data set and presented.

In this paper, hyper-spectral images are employed for colour shade grading tile classification. Our goal is to design a machine vision system that can estimate the sufficient similarity of tiles, or same appearance to the human eye. We use spectral representations of colour, and spectral features to distinguish tiles of the same class but with subtle differences in their colour shade grading. A laboratory system for colour shade grading measurements is built and a method for calibrating such a system is introduced. Experiments with three classes of tiles are presented and discussed.

We address the problem of complexity reduction in hyperspectral image unmixing. When the hyperspectral images are highly resoluted, we propose to select a limited number of pixels, therefore reducing dramatically the size of the data. Then, the related mixtures are used as inputs to a positive source separation algorithm. Our pixel selection procedure is based on a convex cone analysis of the data mixtures; indeed, positive mixtures of sources are embedded in a convex cone whose boundary contains complete available information regarding the sources. We search for the least number of mixtures embedding the convex cone and then store the corresponding pixel indices as the selected pixels. We apply this method to the analysis of hyperspectral images of bacterial cells obtained on a confocal microscope. The bacterial cells, acting as whole-cell biosensors, display great potential as living transducers in sensing applications.

The goal of this research is to make photogrammetric surveys of the walls of Cortona from 2012 accessible using new methodologies for recovering photographic material. This will allow a team of archaeologists to carry out a virtual reconnaissance of the surveyed stretch of wall as well as provide the basis for future investigations into any potential changes that may have occurred in the wall since 2012. Photogrammetry is a widely used technique in archaeology that can help researchers accurately measure, reconstruct, and analyze different architectural components of the wall. By using state-of-the-art photogrammetric techniques, including advanced computer vision algorithms, our team aims to produce high-quality 3D models and accurate measurements of different parts of the wall. The results of this research project will enable archaeologists to gain a more comprehensive understanding of the layout of the fortifications and the role of the Cortonese walls in the historical context o...