nils-herrmann/clean_scirepeval huggingface dataset:
Improved Local Spectral Unmixing of hyperspectral data using an algorithmic regularization path for collaborative sparse regression

Local Spectral Unmixing (LSU) methods perform the unmixing of hyperspectral data locally in regions of the image. The endmembers and their abundances in each pixel are extracted region-wise, instead of globally to mitigate spectral variability effects, which are less severe locally. However, it requires the local estimation of the number of endmembers to use. Algorithms for intrinsic dimensionality (ID) estimation tend to overestimate the local ID, especially in small regions. The ID only provides an upper bound of the application and scale dependent number of endmembers, which leads to extract irrelevant signatures as local endmembers, associated with meaningless local abundances. We propose a method to select in each region the best subset of the locally extracted endmembers. Collaborative sparsity is used to detect spurious endmembers in each region and only keep the most influent ones. We compute an algorithmic regularization path for this problem, giving access to the sequence of successive active sets of endmembers when the regularization parameter is increased. Finally, we select the optimal set in the sense of the Bayesian Information Criterion (BIC), favoring models with a high likelihood, while penalizing those with too many endmembers. Results on real data show the interest of the proposed approach.
Combining rotation forest and multi-scale segmentation for the classification of hyperspectral data

In order to tackle the main issues (high-precision classifier and spatial information model) in the classification of hyperspectral images, we proposed a new approach that combines two powerful techniques: Rotation Forest and multi-scale segmentation. Multi-scale segmentation was used to model the spatial information for different classes. Then, the objects produced by multi-scale segmentation was treated as the input of the Rotation Forest classifier. Furthermore, we combined the classification results generated by Rotation Forest and multi-scale segmentation to generate the final result. Experimental results demonstrate the excellent performances of this proposed approach.
Stochastically based wet snow mapping with SAR DATA

This paper proposes the new method for wet snow mapping using SAR data. It represents a modified version of the existing Nagler's mapping method, based on winter/summer image comparison, which is considered as the classic one. Instead of the existing unique threshold, a variable threshold matrix (function of the local incidence angle for each pixel) is proposed, based on dry and wet snow backscattering simulation results. The new membership decision method (with the respect to the dry/snow classes) is introduced. It considers the intensity ratio as a stochastical process: the probability that “the intensity ratio is smaller than the corresponding dry/wet snow determined threshold” is larger than the desired confidence level.
Hedges detection using local directional features and support vector data description

The detection of hedges in very high spatial resolution remote sensing image is discussed in the paper. A spatial-spectral detector is proposed. The spatial information is modeled per pixel as the local orientation of the structure to which the pixel belongs. The local orientation is computed from the morphological directional profile built with a series of linear openings in several directions. These features are used as inputs to a support vector data description, a detection algorithm. Experimental results on a real satellite image show that the local orientation helps in discriminating hedges from other woody elements, which is not possible using the spectral information only.
Hyperspectral and multispectral data fusion based on nonlinear unmixing

Data fusion of low spatial-resolution hyperspectral (HS) and high spatial-resolution multispectral (MS) images based on a linear mixing model (LMM) enables the production of high spatial-resolution HS data with small spectral distortion. This paper extends the LMM based HS-MS data fusion to nonlinear mixing model using a bilinear mixing model (BMM), which considers second scattering of photons between two distinct materials. A generalized bilinear model (GBM) is able to deal with the underlying assumptions in the BMM. The GBM is applied to HS-MS data fusion to produce high-quality fused data regarding multiple scattering effect. Semi-nonnegative matrix factorization (Semi-NMF), which can be easily incorporated with the existing LMM based fusion method, is introduced as a new optimization method for the GBM unmixing. Comparing with the LMM based HS-MS data fusion, the proposed method showed better results on synthetic datasets.
Class-Oriented Weighted Kernel Sparse Representation With Region-Level Kernel for Hyperspectral Imagery Classification

As a nonlinear extension of traditional sparse representation-based classifier (SRC), kernel SRC (KSRC) has shown its excellent performance for hyperspectral image (HSI) classification, by mapping the nonlinearly separable samples into high-dimensional feature space. However, the rich locality structure of HSI contains more discriminative information, which should be considered in KSRC. We intend to incorporate the locality structure and kernel method into a unified SR-based framework by a local spatial kernel. As a powerful texture descriptor, local binary patterns (LBP) was used to extract local feature for remote sensing. Region-level kernels are applied to calculate the distance between two LBP histogram features. To discover nonlinear similarity information between test and training samples, we integrate the LBP feature into spatial region-level kernel for HSI classification. Then, we propose a weighted kernel sparse representation classifier optimized via class-oriented strategy, which combines local structure information and SRC in the kernel feature space based on spatial region-level kernel. Experimental results on three open HSIs demonstrate that the proposed method achieves better classification performance than other state-of-the-art classification methods.
Dynamical Spectral Unmixing of Multitemporal Hyperspectral Images

In this paper, we consider the problem of unmixing a time series of hyperspectral images. We propose a dynamical model based on linear mixing processes at each time instant. The spectral signatures and fractional abundances of the pure materials in the scene are seen as latent variables, and assumed to follow a general dynamical structure. Based on a simplified version of this model, we derive an efficient spectral unmixing algorithm to estimate the latent variables by performing alternating minimizations. The performance of the proposed approach is demonstrated on synthetic and real multitemporal hyperspectral images.
Gradient Optimization for multiple kernel's parameters in support vector machines classification

The subject of this work is the model selection of kernels with multiple parameters for support vector machines (SVM), with the purpose of classifying hyperspectral remote sensing data. During the training process, the kernel parameters need to be tuned properly. In this work a gradient descent based algorithm is used to estimate the parameters. The selection of multiple parameters is addressed, and an approach based on the analysis of the variance values of individual bands was proposed. Several state of the art kernels were tested. Experiments were conducted on real hyperspectral data. Results obtained with the different approaches/kernels were compared statistically, and showed good results in terms classification accuracies and processing time.
Full Scale Regression-Based Injection Coefficients for Panchromatic Sharpening

Pansharpening is usually related to the fusion of a high spatial resolution but low spectral resolution (panchromatic) image with a high spectral resolution but low spatial resolution (multispectral) image. The calculation of injection coefficients through regression is a very popular and powerful approach. These coefficients are usually estimated at reduced resolution. In this paper, the estimation of the injection coefficients at full resolution for regression-based pansharpening approaches is proposed. To this aim, an iterative algorithm is proposed and studied. Its convergence, whatever the initial guess, is demonstrated in all the practical cases and the reached asymptotic value is analytically calculated. The performance is assessed both at reduced resolution and at full resolution on four data sets acquired by the IKONOS sensor and the WorldView-3 sensor. The proposed full scale approach always shows the best performance with respect to the benchmark consisting of state-of-the-art pansharpening methods.
Linear unmixing of multidate hyperspectral imagery for crop yield estimation

In this paper, we have evaluated unsupervised unmixing approach (VCA) for the application of crop yield estimation. The results show that abundance maps of the vegetation extracted by both approaches are strongly correlated to the yield data (the correlation coefficients are between 0.7 to 0.8). The results validate the higher efficiency of the unsupervised unmixing approaches compared with the supervised methods presented [13] for the purpose of yield estimation. In addition, the unmixing was performed on the hyperspectral images of the same fields, but taken on two different dates. The results show that the correlations between the vegetation abundances and the yield image might change in terms of the observation dates. However, by combining the vegetation abundances extracted on different dates (by computing their products or the square roots of the products), the correlations are significantly improved so that we can always obtain the highest correlations.
New hyperspectral data representation using binary partition tree

The optimal exploitation of the information provided by hyperspectral images requires the development of advanced image processing tools. This paper introduces a new hierarchical structure representation for such images using binary partition trees (BPT). Based on region merging techniques using statistical measures, this region-based representation reduces the number of elementary primitives and allows a more robust filtering, segmentation, classification or information retrieval. To demonstrate BPT capabilites, we first discuss the construction of BPT in the specific framework of hyperspectral data. We then propose a pruning strategy in order to perform a classification. Labelling each BPT node with SVM classifiers outputs, a pruning decision based on an impurity measure is addressed. Experimental results on two different hyperspectral data sets have demonstrated the good performances of a BPT-based representation
A Variational Pansharpening Approach Based on Reproducible Kernel Hilbert Space and Heaviside Function

Pansharpening is an important application in remote sensing image processing. It can increase the spatial-resolution of a multispectral image by fusing it with a high spatial-resolution panchromatic image in the same scene, which brings great favor for subsequent processing such as recognition, detection, etc. In this paper, we propose a continuous modeling and sparse optimization based method for the fusion of a panchromatic image and a multispectral image. The proposed model is mainly based on reproducing kernel Hilbert space (RKHS) and approximated Heaviside function (AHF). In addition, we also propose a Toeplitz sparse term for representing the correlation of adjacent bands. The model is convex and solved by the alternating direction method of multipliers which guarantees the convergence of the proposed method. Extensive experiments on many real datasets collected by different sensors demonstrate the effectiveness of the proposed technique as compared with several state-of-the-art pansharpening approaches.
River network detection on simulated swot images based on curvilinear denoising and morphological detection

In this paper, a new technique is presented to detect the river networks in simulated SWOT images. The proposed algorithm is based on a noise reduction step followed by a directional morphological filter. In this work, the speckle noise reduction has been achieved by using a Curvelet-based filter preserving the structures of interest. After the filtering task, a river contrast enhancement has been presented by using the Path-Opening filter. This morphological filtering has retained the curvilinear structures on the image independently of their orientation. Hence, the river detection has been possible by a simple thresholding on the Path-Opening result. The obtained results are evaluated using a visual inspection and a quantitative evaluation. The potential of the proposed algorithm has been evaluated by studying the robustness of the parameters.
Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles

Classification of hyperspectral data with high spatial resolution from urban areas is discussed. An approach has been proposed which is based on using several principal components from the hyperspectral data and build morphological profiles. These profiles can be used all together in one extended morphological profile. A shortcoming of the approach is that it is primarily designed for classification of urban structures and it does not fully utilize the spectral information in the data. Similarly, a pixel-wise classification solely based on the spectral content can be performed, but it lacks information on the structure of the features in the image. An extension is proposed in this paper in order to overcome these dual problems. The proposed method is based on the data fusion of the morphological information and the original hyperspectral data: the two vectors of attributes are concatenated. After a reduction of the dimensionality using Decision Boundary Feature Extraction, the final classification is achieved using a Support Vector Machines 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 of approaches based on the use of morphological profiles based on PCs only and conventional spectral classification.
A Pansharpening Method Based on the Sparse Representation of Injected Details

The application of sparse representation (SR) theory to the fusion of multispectral (MS) and panchromatic images is giving a large impulse to this topic, which is recast as a signal reconstruction problem from a reduced number of measurements. This letter presents an effective implementation of this technique, in which the application of SR is limited to the estimation of missing details that are injected in the available MS image to enhance its spatial features. We propose an algorithm exploiting the details self-similarity through the scales and compare it with classical and recent pansharpening methods, both at reduced and full resolution. Two different data sets, acquired by the WorldView-2 and IKONOS sensors, are employed for validation, achieving remarkable results in terms of spectral and spatial quality of the fused product.
Generalized bilinear model based nonlinear unmixing using semi-nonnegative matrix factorization

Nonlinear spectral mixing models have recently been receiving attention in hyperspectral image processing. This work presents a novel optimization method for nonlinear unmixing based on a generalized bilinear model (GBM), which considers second-order scattering effects. Semi-nonnegative matrix factorization is used for optimization to process a whole image in a matrix form. The proposed method is applied to an airborne hyperspectral image with many endmembers and shows good performance both in unmixing quality and computational cost with simple implementation. The effect of endmember extraction on nonlinear unmixing is investigated and the impact of the nonlinearity on abundance maps is demonstrated.
On the recurrence plot analysis method behaviour under scaling transform

In the last decade, the applications of the recurrence plot analysis method make it a valuable alternative to the time-frequency and time-scale tools. As it was initially developed for the study of dynamical systems, and was later used in nonlinear time series analysis, the question of using it as a signal processing tool has not been put into discussion yet. In this field the projective techniques are largely used, with good results. Nevertheless, they also have some limitations — especially regarding transient signal processing. But this kind of signals are ubiquitous in real world. In addition, propagation through various media as well as on multiple paths lead to delayed, attenuated and dilated versions of the original transients. In this paper we study the behaviour of the recurrence plot analysis method in the context of analyzing some finite duration signals being subject to rescalings of the amplitude and time axes. This study is a starting point in employing the analysis of recurrences in investigations of a large class of real world signals.
On the Use of a New Additive Kernel for Change Detection using SVM

In the context of change detection and due to the multitude of change scenarios, the objective is to build a generic change detection system. For many technical and operational reasons the Support Vector Machines (SVM) algorithm is used. One of the crucial steps when using the SVM algorithm is the choice of the kernel function. With the lack of a priori information the choice of the kernel function may be difficult for the user. In this paper several techniques for constructing a suitable kernel function obtained from the data are proposed.
Class-Separation-Based Rotation Forest for Hyperspectral Image Classification

In this letter, we propose a new version of the rotation forest (RoF) method for the pixelwise classification of hyperspectral images. RoF, which is an ensemble of decision tree classifiers, uses random feature selection and data transformation techniques (i.e., principal component analysis) to improve both the accuracy of base classifiers and the diversity within the ensemble. Traditional RoF performs data transformation on the training samples of each subset. In order to further improve the performance of RoF, the data transformation is separately performed on each class, extracting sets of transformation matrices that are strictly dependent on the training samples of each single class. The approach, namely, class-separation-based RoF (RoFCS), is experimentally investigated on a hyperspectral image collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. Experimental results demonstrate that the proposed methodology achieves excellent performances, in comparison with random forest and RoF classifiers.
Mean–Standard Deviation Representation of Sonar Images for Echo Detection: Application to SAS Images

This paper addresses the detection of underwater mines echoes with application to synthetic aperture sonar (SAS) imaging. A detection method based on local first- and second-order statistical properties of the sonar images is proposed. It consists of mapping the data onto the mean-standard deviation plane highlighting these properties. With this representation, an adaptive thresholding of the data enables the separation of the echoes from the reverberation background. The procedure is automated using an entropy criterion (setting of a threshold). Applied on various SAS data sets containing both proud and buried mines, the proposed method positively compares to the conventional amplitude threshold detection method. The performances are evaluated by means of receiver operating characteristic (ROC) curves.
How Transferable Are Spatial Features for the Classification of Very High Resolution Remote Sensing Data?

Knowledge transfer for the classification of very high resolution panchromatic data over urban area is investigated. Invariant feature are extracted with some morphological processing. The well-known spectral angle mapper (SAM) is proposed as a measure of transferability. Support vector machines (SVMs) are used to fit a separating hyperplane in a vector space defined by the extracted spatial features. The hyperplane is then used to classify other data set without any new training. Several experiments are presented. Results confirm the usefulness of spatial feature when the classification of two images from two separates data set is considered.
Foreword to the Special Issue on Hyperspectral Image and Signal Processing

T HE very high spectral resolution of remotely sensed hyperspectral data , rooted in technological, modeling and processing advances, has fostered a strong interest in this image modality at an unprecedented rate in recent years. In fact, the very high spectral resolution of hyperspectral data offers very significant potential in the identification of materials and their properties [2]. However, the high dimensionality of hyperspectral data (and its usually low spatial resolution) raises a series of new challenges on several fronts, namely, in signal and image processing, physical modeling, sensor design and calibration, applications and computationally efficient processing . The aforementioned topics were addressed in the IEEE Geoscience and Remote Sensing Society (GRSS) Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).1 After two successful editions in Grenoble, France (2009), and Reykjavik, Iceland (2010), the 2011 edition of WHISPERS took place in Lisbon. The venue was the Instituto Superior Técnico (IST), the largest and most reputed school of Engineering, Science and Technology and Architecture in Portugal. The general chairs of WHISPERS 2011 were Prof. José M. Bioucas-Dias from IST and Prof. Antonio Plaza from the University of Extremadura, Spain. The program chair was Prof. Jocelyn Chanussot from GIPSA-Lab, Grenoble Institute of Technology, France. After a careful review process, in which each submission received an average of 2.5 anonymous reviews, a total of 126 papers were accepted for presentation and publication in the proceedings of WHISPERS 2011. Of these, 92 were orally presented while the remaining 34 were presented as posters. In total, fourteen regular sessions (five on signal and image processing, three on physical modeling, two on sensor design, and five on applications) were organized, covering a wide spectrum of techniques and disciplines in this research area). Three special dedicated sessions were organized on advanced topics for hyperspectral data exploitation, including spectral unmixing, machine learning, and target detection. The technical program promoted the dissemination of research results and technical advances in the aforementioned topics and offered a fertile discussion forum for researchers coming from different areas and backgrounds. The technical program of WHISPERS 2011 also featured three plenary talks delivered by prestigious and highly recognized experts worldwide. Prof. Melba Crawford, from Purdue University, USA, delivered a talk entitled “Nonlinear manifolds for feature extraction: opportunities and challenges.” Dr. Alexander Held, from CSIRO Division of Marine and
Decision level fusion in classification of hyperspectral data from urban areas

Classification of hyperspectral data with high spatial resolution using both spatial and spectral approaches is discussed. The spatial approach is based on mathematical morphology. In the method, several principal components (PCs) from the hyperspectral data are used. From each of the PCs, a morphological profile is built. These profiles are used together in one extended morphological profile, which is then classified with a neural network. The spectral classification approach is based on maximum likelihood classification and nonparametric weighted feature extraction (NWFE). The results from the spectral and spatial modeling are finally fused together using several different fusion rules. Experimental results are given on a hyperspectral data from an urban area.