nils-herrmann/clean_scirepeval huggingface dataset:
Learning a low-coherence dictionary to address spectral variability for hyperspectral unmixing

This paper presents a novel spectral mixture model to address spectral variability in inverse problems of hyperspectral unmixing. Based on the linear mixture model (LMM), our model introduces a spectral variability dictionary to account for any residuals that cannot be explained by the LMM. Atoms in the dictionary are assumed to be low-coherent with spectral signatures of endmembers. A dictionary learning technique is proposed to learn the spectral variability dictionary while solving unmixing problems simultaneously. Experimental results on synthetic and real datasets demonstrate that the performance of the proposed method is superior to state-of-the-art methods.
Order filter with progressively decimated filtering window: application to colour image enhancement

The classical definition of vector order filters consists in selecting the pixel that minimizes the cumulated distance to the other pixels of the filtering window. The "most representative" pixel of the filtering window is then selected and the noise is thus reduced. But, when the filtering window reaches a transition, the result is biased and the smoothing is not optimal any more. The proposed technique consists in progressively decimating the filtering window by suppressing the pixels that maximize the cumulated distance. The process is then iterated (computation of the cumulated distances among the remaining pixels and suppression of the less typical pixels) until one single pixel remains. It is then selected for the filter output. In practical cases, the number of iterations is limited. Applied on colour images, the filter results in an enhancement of the edges and in an automatic registration of the different components. The counterpart is a slight degradation of the smoothing performance in homogeneous regions. The paper details the proposed method and compares it with other enhancement filters.
A multiple classifier approach for spectral-spatial classification of hyperspectral data

A new multiple classifier method for spectral-spatial classification of hyperspectral images is proposed. Several classifiers are used independently to classify an image. For every pixel, if all the classifiers have assigned this pixel to the same class, the pixel is kept as a marker, i.e., a seed of the spatial region, with the corresponding class label. We propose to use spectral-spatial classifiers at the preliminary step of the marker selection procedure, each of them combining the results of a pixel-wise classification and a segmentation map. Different segmentation approaches lead to different classification results. Furthermore, a minimum spanning forest is built, where each tree is rooted on a classification-driven marker and forms a region in the spectral-spatial classification map. Experimental results are presented on a 103-band ROSIS image of the University of Pavia, Italy. The proposed method significantly improves classification accuracies, when compared to previously proposed classification techniques.
Physic based aggregation model for the unmixing of temperature and optical properties in the infrared domain

The new generation of airborne hyperspectral image sensors sensitive in the thermal infrared domain gives the opportunity for new urban applications like classification, thermal budget at building level, etc. Nevertheless, due to the high spatial variability of such landscape, the signal acquired by each pixel remains complex as it results of the contribution of several radiative signals coming from different materials. The understanding of such signal requires the developement of an aggregation model that modelize the optical properties and surface temperature aggregating at different spatial scales. This paper proposes to justify the unicity of the aggregation model, proposed by Fontanilles et al, on LWIR spectral range (8 - 14 μm) for small temperature variations (≤ 20K).
Linear Versus Nonlinear PCA for the Classification of Hyperspectral Data Based on the Extended Morphological Profiles

Morphological profiles (MPs) have been proposed in recent literature as aiding tools to achieve better results for classification of remotely sensed data. MPs are in general built using features containing most of the information content of the data, such as the components derived from principal component analysis (PCA). Recently, nonlinear PCA (NLPCA), performed by autoassociative neural network, has emerged as a good unsupervised technique to fit the information content of hyperspectral data into few components. The aim of this letter is to investigate the classification accuracies obtained using extended MPs built from the features of NPCA. A comparison of the two approaches has been validated on two different data sets having different spatial and spectral resolutions/coverages, over the same ground truth, and also using two different classification algorithms. The results show that NLPCA permits one to obtain better classification accuracies than using linear PCA.
Unsupervised classification and spectral unmixing for sub-pixel labelling

The unsupervised classification of hyperspectral images containing mixed pixels is addressed in this paper. Hyperspectral images are characterized by a trade-off between the spectral and the spatial resolution, this leading to data sets containing mixed pixels, e.g. pixels jointly occupied by more than a single land cover class. In , a preliminary research based on spectral unmixing concepts was conducted, in order to handle mixed pixels and to obtain thematic maps at a finer spatial resolution. In this work, we extend the investigation by proposing a new methodology based on image clustering. Experiments conducted on real data show the comparative effectiveness of the proposed method, which provides good results in terms of accuracy and is less sensitive to pixels with extreme values of reflectance.
Decision Fusion for the Classification of Urban Remote Sensing Images

The classification of very high resolution remote sensing images from urban areas is addressed by considering the fusion of multiple classifiers which provide redundant or complementary results. The proposed fusion approach is in two steps. In a first step, data are processed by each classifier separately, and the algorithms provide for each pixel membership degrees for the considered classes. Then, in a second step, a fuzzy decision rule is used to aggregate the results provided by the algorithms according to the classifiers' capabilities. In this paper, a general framework for combining information from several individual classifiers in multiclass classification is proposed. It is based on the definition of two measures of accuracy. The first one is a pointwise measure which estimates for each pixel the reliability of the information provided by each classifier. By modeling the output of a classifier as a fuzzy set, this pointwise reliability is defined as the degree of uncertainty of the fuzzy set. The second measure estimates the global accuracy of each classifier. It is defined a priori by the user. Finally, the results are aggregated with an adaptive fuzzy operator ruled by these two accuracy measures. The method is tested and validated with two classifiers on IKONOS images from urban areas. The proposed method improves the classification results when compared with the separate use of the different classifiers. The approach is also compared with several other fuzzy fusion schemes
Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles

A method is proposed for the classification of urban hyperspectral data with high spatial resolution. The approach is an extension of previous approaches and uses both the spatial and spectral information for classification. One previous approach is based on using several principal components (PCs) from the hyperspectral data and building several morphological profiles (MPs). These profiles can be used all together in one extended MP. A shortcoming of that approach is that it was primarily designed for classification of urban structures and it does not fully utilize the spectral information in the data. Similarly, the commonly used pixelwise classification of hyperspectral data is solely based on the spectral content and lacks information on the structure of the features in the image. The proposed method overcomes these problems and is based on the fusion of the morphological information and the original hyperspectral data, i.e., the two vectors of attributes are concatenated into one feature vector. After a reduction of the dimensionality, the final classification is achieved by using a support vector machine classifier. The proposed approach is tested in experiments on ROSIS data from urban areas. Significant improvements are achieved in terms of accuracies when compared to results obtained for approaches based on the use of MPs based on PCs only and conventional spectral classification. For instance, with one data set, the overall accuracy is increased from 79% to 83% without any feature reduction and to 87% with feature reduction. The proposed approach also shows excellent results with a limited training set.
Automatic Detection of Rivers in High-Resolution SAR Data

Remote sensing plays a major role in supporting decision-making and surveying compliance of several multilateral environmental treaties. In this paper, we present an approach for supporting monitoring compliance of river networks in context of the European Water Framework Directive. Only a few approaches have been developed for extracting river networks from satellite data and usually they require manual input, which seems not feasible for automatic and operational application. We propose a method for the automatic extraction of river structures in TerraSAR-X data. The method is based on mathematical morphology and supervised image classification, using automatically selected training samples. The method is applied on TerraSAR-X images from two different study sites. In addition, the results are compared to an alternative method, which requires manual user interaction. The detailed accuracy assessment shows that the proposed method achieves accurate results (Kappa ~ 0.7) and performs almost similar in terms of accuracy, when compared to the alternative approach. Moreover, the proposed method can be applied on various datasets (e.g., multitemporal, multisensoral and multipolarized) and does not require any additional user input. Thus, the highly flexible approach is interesting in terms of operational monitoring systems and large scale applications.
High-Resolution SAR Interferometry: Estimation of Local Frequencies in the Context of Alpine Glaciers

Synthetic aperture radar (SAR) interferometric data offer the opportunity to measure temperate glacier surface topography and displacement. The increase of the resolution provided by the most recent SAR systems has some critical implications. For instance, a reliable estimate of the phase gradient can only be achieved by using interferogram local frequencies. In this paper, an original two-step method for estimating local frequencies is proposed. The 2-D phase signal is considered to have two deterministic components corresponding to low-resolution (LR) fringes and high-resolution (HR) patterns due to the local microrelief, respectively. The first step of the proposed algorithm consists in the LR phase flattening. In the second step, the local HR frequencies are estimated from the phase 2-D autocorrelation function computed on adaptive neighborhoods. This neighborhood is the set of connected pixels belonging to the same HR spatial feature and respecting the ldquolocal stationarityrdquo hypothesis. Results with both simulated TerraSAR-X interferograms and real airborne E-SAR images are presented to illustrate the potential of the proposed method.
Airborne remote sensing of vineyards for the detection of dead vine trees

Airborne remote sensing technology can be used to accurately monitor vineyards. In this paper we present a method to detect missing or dead trees. This is achieved by exploiting the periodic structure of a vineyard: equally spaced trees are planted in parallel rectilinear rows. This clearly appears on the spectrum which can be analyzed using the Radon transform. Simple morphological operators then enable the detection of unusal spaces within the plantation rows, corresponding to missing trees.
Evaluation of the New Information in the ${H}/\alpha$ Feature Space Provided by ICA in PolSAR Data Analysis

The Cloude and Pottier <inline-formula> <tex-math notation="LaTeX">$H/\alpha $ </tex-math></inline-formula> feature space is one of the most employed methods for unsupervised polarimetric synthetic aperture radar (PolSAR) data classification based on incoherent target decomposition (ICTD). The method can be split in two stages: the retrieval of the canonical scattering mechanisms present in an image cell and their parameterization. The association of the coherence matrix eigenvectors to the most dominant scattering mechanisms in the analyzed pixel introduces unfeasible regions in the <inline-formula> <tex-math notation="LaTeX">$H/\alpha $ </tex-math></inline-formula> plane. This constraint can compromise the performance of detection, classification, and geophysical parameter inversion algorithms that are based on the investigation of this feature space. The independent component analysis (ICA), recently proposed as an alternative to eigenvector decomposition, provides promising new information to better interpret non-Gaussian heterogeneous clutter (inherent to high-resolution SAR systems) in the frame of polarimetric ICTDs. Not constrained to any orthogonality between the estimated scattering mechanisms that compose the clutter under analysis, ICA does not introduce any unfeasible region in the <inline-formula> <tex-math notation="LaTeX">$H/\alpha $ </tex-math></inline-formula> plane, increasing the range of possible natural phenomena depicted in the aforementioned feature space. This paper addresses the potential of the new information provided by the ICA as an ICTD method with respect to Cloude and Pottier <inline-formula> <tex-math notation="LaTeX">$H/\alpha $ </tex-math></inline-formula> feature space. A PolSAR data set acquired in October 2006 by the E-SAR system over the upper part of the Tacul glacier from the Chamonix Mont Blanc test site, France, and a RAMSES X-band image acquired over Brétigny, France, are taken into consideration to investigate the characteristics of pixels that may fall outside the feasible regions in the <inline-formula> <tex-math notation="LaTeX">$H/\alpha $ </tex-math></inline-formula> plane that arise when the eigenvector approach is employed.
Multi-Modal Change Detection, Application to the Detection of Flooded Areas: Outcome of the 2009–2010 Data Fusion Contest

The 2009-2010 Data Fusion Contest organized by the Data Fusion Technical Committee of the IEEE Geoscience and Remote Sensing Society was focused on the detection of flooded areas using multi-temporal and multi-modal images. Both high spatial resolution optical and synthetic aperture radar data were provided. The goal was not only to identify the best algorithms (in terms of accuracy), but also to investigate the further improvement derived from decision fusion. This paper presents the four awarded algorithms and the conclusions of the contest, investigating both supervised and unsupervised methods and the use of multi-modal data for flood detection. Interestingly, a simple unsupervised change detection method provided similar accuracy as supervised approaches, and a digital elevation model-based predictive method yielded a comparable projected change detection map without using post-event data.
Validation of spectral unmixing algorithms applied on CRISM/MRO hyperspectral images

As the volume of hyperspectral data for planetary exploration increases, efficient yet accurate algorithms are decisive for their analysis. In this article, the capability of spectral unmixing for analyzing hyperspectral images from Mars is investigated. For that purpose, we consider the Russell megadune observed by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) and the High Resolution Imaging Science Experiment (HiRISE) instruments. In late winter, this area of Mars is appropriate for testing linear unmixing techniques because of the geographical coexistence of seasonal CO2 ice and defrosting dusty features, which is not resolved by CRISM. Linear unmixing is carried out on a selected CRISM image by a seven state-of-the-art approaches based on different principles. Processing of HiRISE imagery allows the construction of a ground truth in the form of a reference abundance map related to the defrosting features. Validation of abundances estimated by spectral unmixing is carried out in an independent and quantitative manner by comparison to the ground truth.
Mahalanobis kernel based on probabilistic principal component

A kernel adapted to the spectral dimension of hyperspectral images is proposed in this paper. A distance based on a statistical cluster model is used to construct a radial kernel. This class specific kernel realizes a compromise between a conventional Gaussian kernel and a Gaussian kernel on the first principal components of the considered class. An automatic gradient optimization is used to select the optimal hyperparameters. Experimental results on a real hyperspectral image show the kernel is effective compared to the conventional Gaussian kernel. Furthermoren the proposed kernel is less sensitive to one hyperparameter compared to the Gaussian kernel applied on the first principal components of the data.
Speeding up Support Vector Machine (SVM) image classification by a kernel series expansion

Due to their flexibility, and capacity to handle high dimensional vectorial data, support vector machines (SVMs) have become the reference for remote sensing imagery classification. However when processing large amounts of data the SVM classification could be a time consuming process. In this paper a new decomposition scheme of the SVM decision function is proposed. The decomposition is based on using the Taylor series expansion to approximate the kernel function. Then, using the results of the optimization problem of the SVM after the learning phase, this expansion is used to obtain an approximate decision function that provides a trade-off between the classification accuracy and the processing time. This speeds-up the SVM classification if limited processing time is available and favors accuracy if sufficient processing time is available.
Context-Adaptive Pansharpening Based on Image Segmentation

Pansharpened images are widely used synthetic representations of the Earth surface characterized by both a high spatial resolution and a high spectral diversity. They are usually generated by extracting spatial details from a high-resolution PANchromatic image and by injecting them into a low spatial resolution multispectral image. The details injection is performed through injection coefficients, whose values can be either uniform for the whole image (global methods) or spatially variant (context-adaptive (CA) approaches). In this paper, we propose a CA approach in which the injection coefficients are estimated over image segments achieved through a binary partition tree segmentation algorithm. The approach is applied to two credited pansharpening algorithms based on the Gram–Schmidt orthogonalization procedure and the generalized Laplacian pyramid technique. The performance assessment is performed using two different data sets acquired by the QuickBird and the WorldView-3 satellites. The validation procedure, both at full and at reduced resolution, shows the suitability of the proposed approach, which reaches a good tradeoff between accuracy and computational burden.
Low-Rank Decomposition and Total Variation Regularization of Hyperspectral Video Sequences

Hyperspectral video sequences (HVSs) are well suited for gas plume detection (GPD). The high spectral resolution allows the detection of chemical clouds even when they are optically thin. Processing this new type of video sequences is challenging and requires advanced image and video analysis algorithms. In this paper, we propose a novel method for GPD recorded in HVSs. Based on the assumption that the background is stationary and the gas plume is moving, the proposed method separates the background from the gas plume via a low-rank and sparse decomposition. Furthermore, taking into consideration that the gas plume is continuous in both spatial and temporal dimensions, we include total variation regularization in the constrained minimization problem, which we solve using the augmented Lagrangian multiplier method. After applying the above process to each extracted feature, a novel fusion strategy is proposed to combine the information into a final detection result. Experimental results using real data sets indicate that the proposed method achieves very promising GPD performance.
Pansharpening of hyperspectral images: Exploiting data acquired by multiple platforms

Accurate representations of the Earth surface in both spatial and spectral domains are highly desirable in many applications using remotely sensed data. An effective solution is achieved by combining hyperspectral data, which are characterized by a high spectral diversity, with high spatial resolution images, collected by multispectral or panchromatic sensors. In this work, we compare the outcomes provided by fusing single-platform or multi-platform data. We demonstrate that the optimal choice depends on the target spatial resolution to be achieved. To this aim, real images collected by the Hyperion sensor are combined with data acquired by the ALI sensor or the QuickBird sensor assessing the fused outcomes at reduced resolution.
On selecting relevant intrinsic mode functions in empirical mode decomposition: An energy-based approach

Although the empirical mode decomposition is a powerful tool for analyzing complicated datasets, many irrelevant intrinsic mode functions may appear in the decomposition. In this paper, we develop an energy-based method to detect relevant intrinsic mode functions. The new method can be seen as a generalization of techniques that are based on correlation. An experimental study is carried out in different datasets for assessing the performance of the proposed technique.
Urban Mapping Using Coarse SAR and Optical Data: Outcome of the 2007 GRSS Data Fusion Contest

The 2007 data fusion contest that was organized by the IEEE Geoscience and Remote Sensing Data Fusion Technical Committee was dealing with the extraction of a land use/land cover maps in and around an urban area, exploiting multitemporal and multisource coarse-resolution data sets. In particular, synthetic aperture radar and optical data from satellite sensors were considered. Excellent indicators for mapping accuracy were obtained by the top teams. The best algorithm is based on a neural classification enhanced by preprocessing and postprocessing steps.
Classification based marker selection for watershed transform of hyperspectral images

A new method for segmentation and classification of hyper-spectral images is proposed. The method is based on a pixel-wise classification followed by selection of the most reliable classified pixels as markers for watershed segmentation. Furthermore, each marker defined from classification results is associated with a class label. By assigning the class label of each marker to all the pixels within the region grown from this marker, a spectral-spatial classification map is obtained. Experimental results are presented on a 200-band AVIRIS image of the Northwestern Indiana's Indian Pine site. The developed segmentation and classification scheme significantly decreases oversegmentation, improves classification accuracies and provides classification maps with more homogeneous regions, when compared to pixel-wise classification or previously proposed spectral-spatial classification techniques.