nils-herrmann/clean_scirepeval huggingface dataset:
Extraction of minerals on the south pole of the planet Mars by unsupervised linear unmixing of hyperspectral images

In this paper, the ability of unsupervised linear unmixing has been evaluated for the hyperspectral image taken on the south pole of the planet Mars by OMEGA instrument aborad MEX. State-of-art methods of the three steps are tested: i) estimation of the number of endmembers; ii) extraction of endmembers; ii) estimation of the abundances. According to the results, it has been found that the combination ELM-VCA-NCLS has the best performance for obtaining the spectral signatures and the abundances of the three principle chemical species (CO2 ice, water ice and dust) on the south pole of the Mars.
Fusion of hyperspectral and panchromatic images: A hybrid use of indusion and nonlinear PCA

Generally, for optical satellite sensors spatial and spectral resolutions are highly correlated factors. In fact, given the design constraints of these sensors, there is an inverse relation between their spatial and spectral resolution. Thus, the hyperspectral sensors have a high spectral resolution i.e. large number of bands covering the electromagnetic spectrum, but a lower spatial resolution. On the other hand, panchromatic (PAN) images have the highest spatial resolution but no spectral diversity. For better utilization and interpretation, hyperspectral images having both high spectral and spatial resolution are desired. This can be achieved by making use of a high spatial resolution PAN image in the context of pansharpening or image fusion. Several fusion approaches have been proposed in the literature. In this paper we propose the use of a hybrid algorithm combining substitution and injection methods. One of the main challenges in hyperspectral image fusion is the improvement of the spatial resolution, i.e. spatial details while preserving the original spectral information. This requires addition of pertinent spatial details to each band of the HS image. However, due to large number of bands the pansharpening of HS images is computationally expensive. Thus a dimensionality reduction preprocess, compressing the original number of measurements into a lower dimensional space, becomes mandatory. In this paper we propose the use of non-linear principal components instead of the original HS bands as input to a fusion process to enhance the spatial resolution of the HS image.
Support vector machines regression for estimation of forest parameters from airborne laser scanning data

Estimation of forest stand parameters from airborne laser scanning data relies on the selection of laser metrics sets and numerous field plots for model calibration. In mountainous areas, forest is highly heterogeneous and field data collection labour-intensive hence the need for robust prediction methods. The aim of this paper is to compare stand parameters prediction accuracies of support vector machines regression and multiple regression models. Sensitivity of these techniques to the number and type of laser metrics, and use of dimension reduction techniques such as principal component and independent component analyses are also tested. Results show that support vector regression was less accurate but more stable than multiple regression for the prediction of forest parameters.
Context-adaptive Pansharpening based on binary partition tree segmentation

Pansharpening is a successful application of data fusion to remotely sensed data. It aims at obtaining a detailed representation of an Earth's zone both in terms of spatial and spectral resolution. This is done through the fusion of a panchromatic and a multispectral image (having complementary spatial and spectral resolutions) that are acquired simultaneously by several optical satellites. The result of the fusion is commonly achieved by introducing the spatial details, modulated opportunely by gains, in the multispectral one. The injection gains can be estimated globally over the image, or locally, thus obtaining spatially variant values. The latter approach has been proven to achieve better results and it is based on windowing the analyzed image in squared blocks. In this paper we propose a more elaborated concept of locality, as it is based on an opportune segmentation of the target scene. In greater details, we propose to estimate the local injection gains on regions composed of pixel with similar spectral characteristic, as defined by a segmentation. Such local approach is compared to the global one and to the conventional local estimation based on overlapping and non-overlapping blocks. The performances have been assessed by using three real datasets, the first acquired by WorldView-2 and the other two by Pléiades. The analysis evidences the appreciable improvements of the performances with respect to classical schemes.
A joint spatial and spectral SVM’s classification of panchromatic images

The classification of very high resolution panchromatic images from urban areas is addressed. The spectral information, i.e. the gray level of each pixel, does generally not ensure a reliable classification. In this paper, we investigate the use of an area filter to extract information about the inter-pixel dependency. The classification is then performed using a support vector machines (SVM) classifier. Using a linear composition of kernels, we define a kernel using both the spectral (original gray level) and the spatial information. A weighting parameter, controlling the relative importance of each feature, is introduced and tuned during the SVM's training process. Experiments have been conducted on simulated panchromatic Pleiades data over Toulouse, France. Results obtained with the proposed approach is positively compared to those obtained with the standard use of gray value information only and classical SVM formulation.
A Stochastic Minimum Spanning Forest approach for spectral-spatial classification of hyperspectral images

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 hyper-spectral 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.
Unsupervised endmember extraction of martian hyperspectral images

In this paper, we try to identify and quantify the chemical species present on the surface of planet Mars with the help of hyperspectral images provided by the instrument OMEGA . For this purpose, we suppose that the spectrum of each pixel is a linear mixture of the spectra of different endmembers. From this linear mixture hypothesis, our work is divided into two steps. Firstly, we propose a new unsupervised method for estimating the number of endmembers based on the eigenvalues of covariance and correlation matrix of the hyperspectral data. This method is then validated on synthetic data. With the help of the number estimated by the precedent step, we use the Vertex Component Analysis (VCA) to extract the spectra and the abundances of the endmembers. The results on hyperspectral image acquired by the OMEGA instrument are shown.
Detection of Anomalies Produced by Buried Archaeological Structures Using Nonlinear Principal Component Analysis Applied to Airborne Hyperspectral Image

In this paper, airborne hyperspectral data have been exploited by means of Nonlinear Principal Component Analysis (NLPCA) to test their effectiveness as a tool for archaeological prospection, evaluating their potential for detecting anomalies related to buried archaeological structures. In the literature, the NLPCA was used to decorrelate the information related to a hyperspectral image. The resulting nonlinear principal components (NLPCs) contain information related to different land cover types and biophysical properties, such as vegetation coverage or soil wetness. From this point of view, NLPCA applied to airborne hyperspectral data was exploited to test their effectiveness and capability in highlighting the anomalies related to buried archaeological structures. Each component obtained from the NLPCA has been interpreted in order to assess any tonal anomalies. As a matter of a fact, since every analyzed component exhibited anomalies different in terms of size and intensity, the Separability Index (SI) was applied for measuring the tonal difference of the anomalies with respect to the surrounding area. SI has been evaluated for determining the potential of anomalies detection in each component. The airborne Multispectral Infrared and Visible Imaging Spectrometer (MIVIS) images, collected over the archaeological Park of Selinunte, were analyzed for this purpose. In this area, the presence of remains, not yet excavated, was reported by archaeologists. A previous analysis of this image, carried out to highlight the buried structures, appear to match the archaeological prospection. The results obtained by the present work demonstrate that the use of the NLPCA technique, compared to previous approaches emphasizes the ability of airborne hyperspectral images to identify buried structures. In particular, the adopted data processing flow chart (i.e., NLPCA and SI techniques, data resampling criteria and anomaly evaluations criteria) applied to MIVIS airborne hyperspectral data, collected over Selinunte Archaeological Park, highlighted the ability of the NLPCA technique in emphasizing the anomalies related to the presence of buried structure.
Spectral–Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques

A new spectral-spatial classification scheme for hyperspectral images is proposed. The method combines the results of a pixel wise support vector machine classification and the segmentation map obtained by partitional clustering using majority voting. The ISODATA algorithm and Gaussian mixture resolving techniques are used for image clustering. Experimental results are presented for two hyperspectral airborne images. The developed classification scheme improves the classification accuracies and provides classification maps with more homogeneous regions, when compared to pixel wise classification. The proposed method performs particularly well for classification of images with large spatial structures and when different classes have dissimilar spectral responses and a comparable number of pixels.
Foreword to the special issue on hyperspectral remote sensing: Theory, methods, and applications

In 2012, the Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), sponsored by IEEE Geoscience and Remote Sensing Society, moved to Shanghai, China. There were 91 oral presentations and 31 posters. Three plenary speakers: Professor Qingxi Tong from Peking University, China, Professor Jean-Pierre Bibring from University of Paris, France, and Professor Susan Ustin from University of California, Davis, USA, introduced their cutting-edge research in this field. Following the success of this fourth WHISPERS edition, it is our great pleasure to introduce this special issue in order to present the most recent developments. A large number of submissions were received, and 29 papers have been accepted after rigorous review. A few of the submissions will be published in the following issues of JSTARS, after the final reviews and revisions are completed. In the remainder of this Foreword, we briefly introduce these 29 accepted papers, which cover several important topics. In particular, 9 of these 29 papers are related to hyperspectral remote sensing applications, which well fit the scope of the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (JSTARS).
Independent Component Discriminant Analysis for hyperspectral image classification

In this paper, the use of Independent Component Discriminant Analysis (ICDA) for remote sensing classification is proposed. ICDA is a non-parametric method for discriminant analysis based on the application of a Bayesian classification rule on a signal composed by independent components. The method is based on the use of Independent Component Analysis (ICA) to choose a transform matrix so that the transformed components are as independent as possible. Then, a non parametric estimation of the density function is computed for each independent component. Finally, the Bayes rule is applied for classification assignment. The obtained results are compared with one of the most used classifier of hyperspectral images (Support Vector Machine) and show the comparative effectiveness of the proposed method.
A support vector regression approach for building seismic vulnerability assessment and evaluation from remote sensing and in-situ data

In this paper, seismic vulnerability assessment is addressed under the umbrella of remote sensing. A study for estimating and evaluating information for assessing seismic vulnerability based on a building basis is presented. The proposed methodology utilizes the capabilities of remote sensing and combines in-situ data tested in the area of Grenoble (France). A map is estimated in agreement with in-situ data, as support information system for seismic risk in the context of building vulnerability assessment. In the methodology proposed, building attributes such as roof identification, building height and characteristic scale are extracted from very high resolution panchromatic data, and an accurate digital elevation model. Support vector machine regression is used to estimate building vulnerability and in-situ data are available for evaluation.
Blind Hyperspectral Unmixing Using an Extended Linear Mixing Model to Address Spectral Variability

Spectral unmixing is one of the main research topics in hyperspectral imaging. It can be formulated as a source separation problem, whose goal is to recover the spectral signatures of the materials present in the observed scene (called endmembers) as well as their relative proportions (called fractional abundances), and this for every pixel in the image. A linear mixture model (LMM) is often used for its simplicity and ease of use, but it implicitly assumes that a single spectrum can be completely representative of a material. However, in many scenarios, this assumption does not hold, since many factors, such as illumination conditions and intrinsic variability of the endmembers, induce modifications on the spectral signatures of the materials. In this paper, we propose an algorithm to unmix hyperspectral data using a recently proposed extended LMM. The proposed approach allows a pixelwise spatially coherent local variation of the endmembers, leading to scaled versions of reference endmembers. We also show that the classic nonnegative least squares, as well as other approaches to tackle spectral variability can be interpreted in the framework of this model. The results of the proposed algorithm on two different synthetic datasets, including one simulating the effect of topography on the measured reflectance through physical modelling, and on two real data sets, show that the proposed technique outperforms other methods aimed at addressing spectral variability, and can provide an accurate estimation of endmember variability along the scene because of the scaling factors estimation.
Hyperspectral unmixing with material variability using social sparsity

We apply social ℓ-norms for the first time to the problem of hyperspectral unmixing while modeling spectral variability. These norms are built with inter-group penalties which are combined in a global intra-group penalization that can enforce selection of entire endmember bundles; this results in the selection of a few representative materials even in the presence of large endmembers bundles capturing each material's variability. We demonstrate improvements quantitatively on synthetic data and qualitatively on real data for three cases of social norms: group, elitist, and a fractional social norm, respectively. We find that the greatest improvements arise from using either the group or fractional flavor.
Advances in Spectral-Spatial Classification of Hyperspectral Images

Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation, and contrast of the spatial structures present in the image. Then, the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple-classifier (MC) system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.
Super-resolution of hyperspectral images using local spectral unmixing

For many remote sensing applications it is preferable to have images with both high spectral and spatial resolutions. On this regards, hyperspectral and multispectral images have complementary characteristics in terms of spectral and spatial resolutions. In this paper we propose an approach for the fusion of low spatial resolution hyperspectral images with high spatial resolution multispectral images in order to obtain superresolution (spatial and spectral) hyperspectral images. The proposed approach is based on the assumption that, since both hyperspectral and multispectral images acquired on the same scene, the corresponding endmembers should be the same. On a first step the hyperspectral image is spectrally down-sampled in order to match the multispectral one. Then an endmember extraction algorithm is performed on the down-sampled hyperspectral image and the successive abundance estimation is performed on the multispectral one. Finally, the extracted endmembers are up-sampled back to the original hyperspectral space and then used to reconstruct the superresolution hyperspectral image according to the abundances obtained from the multispectral image.
Hyperspectral Local Intrinsic Dimensionality

The intrinsic dimensionality (ID) of multivariate data is a very important concept in spectral unmixing of hyperspectral images. A good estimation of the ID is crucial for a correct retrieval of the number of endmembers (the spectral signatures of macroscopic materials) in the image, for dimensionality reduction or for subspace learning, among others. Recently, some approaches to perform spectral unmixing and superresolution locally have been proposed, which require a local estimation of the number of endmembers to use. However, the role of ID in local regions of hyperspectral images has not been properly addressed. Some important issues when dealing with small regions of hyperspectral data can seriously affect the performance of conventional hyperspectral ID estimators. We show that three factors mainly affect local ID estimation: the number of pixels in the local regions, which has to be high enough for the estimations to be relevant, the number of hyperspectral bands which complicates the estimations if the ambient space has a high dimensionality, and the noise, which can be misinterpreted as a signal when its power is important. Here, we review the hyperspectral ID estimators on the literature for local ID estimation, we show how they behave in a local setting on synthetic and real data sets, and we provide some guidelines to make proper use of these estimators in local approaches.
Relationships Between Nonlinear and Space-Variant Linear Models in Hyperspectral Image Unmixing

Hyperspectral image unmixing is a source separation problem whose goal is to identify the signatures of the materials present in the imaged scene (called endmembers), and to estimate their proportions (called abundances) in each pixel. Usually, the contributions of each material are assumed to be perfectly represented by a single spectral signature and to add up in a linear way. However, the main two limitations of this model have been identified as nonlinear mixing phenomena and spectral variability, i.e., the intraclass variability of the materials. The former limitation has been addressed by designing nonlinear mixture models, whereas the second can be dealt with by using (usually linear) space varying models. The typical example is a linear mixing model where the sources can vary from one pixel to the other. In this letter, we show that a recent variability model can also estimate the abundances of nonlinear mixtures to some extent. We make the theoretical connection between nonlinear models and this variability model, and confirm it with experiments on nonlinearly generated synthetic datasets.
Hyperspectral Image Representation and Processing With Binary Partition Trees

The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image-processing tools. This paper proposes the construction and the processing of a new region-based hierarchical hyperspectral image representation relying on the binary partition tree (BPT). This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure. Hence, the BPT succeeds in presenting: 1) the decomposition of the image in terms of coherent regions, and 2) the inclusion relations of the regions in the scene. Based on region-merging techniques, the BPT construction is investigated by studying the hyperspectral region models and the associated similarity metrics. Once the BPT is constructed, the fixed tree structure allows implementing efficient and advanced application-dependent techniques on it. The application-dependent processing of BPT is generally implemented through a specific pruning of the tree. In this paper, a pruning strategy is proposed and discussed in a classification context. Experimental results on various hyperspectral data sets demonstrate the interest and the good performances of the BPT representation.