nils-herrmann/clean_scirepeval huggingface dataset:
Multi-band semiblind deconvolution for pansharpening applications

Pansharpening consists of fusing a multispectral (MS) image together with a panchromatic (PAN) image with the aim of jointly preserving the spectral diversity of the former and the geometric richness of the latter. A crucial step in pansharpening algorithms is the detail extraction. This problem is usually addressed by the means of 2D Gaussian filters matched with the MS sensor's modulation transfer function (MTF). Nevertheless, several issues can affect this characterization (e.g. the MTF's gains at the Nyquist frequency could be not available or unreliable). Thus, in this paper we propose a technique based on blind image deblurring in order to estimate band-dependent spatial detail extraction filters by taking into consideration the possible variability of the MS spatial features along bands. The validation is carried out exploiting two real datasets acquired by the IKONOS and the QuickBird sensors.
Nonnegative CP decomposition of multiangle hyperspectral data: A case study on CRISM observations of Martian ICY surface

The Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) sensor aboard the Mars Reconnaissance Orbiter takes hyperspectral multiangle acquisitions of Martian surface from the top of the atmosphere (TOA) on visible and infrared wavelengths. The Multiangle Approach for Retrieval of Surface Reflectance from CRISM Observations (MARS-ReCO) defined an innovative TOA radiance model and inversion scheme aimed at correcting for aerosols effects taking advantage of the near-simultaneous multiangle CRISM observations. Here, we aim to provide validation evidence of MARS-ReCO by unmixing the estimated multiangle bidirectional reflectance (BRF) from highly reflective and anisotropic icy surfaces at high latitudes with grazing illumination, using a nonnegative CP decomposition. Obtained results are in accordance with other complementary studies, which compose a collaboration effort to validate MARS-ReCO through the cross-validation of different techniques in the absence of ground truth.
Comparison of merging orders and pruning strategies for Binary Partition Tree in hyperspectral data

Hyperspectral imaging segmentation has been an active research area over the past few years. Despite the growing interest, some factors such as high spectrum variability are still significant issues. In this work, we propose to deal with segmentation through the use of Binary Partition Trees (BPTs). BPTs are suggested as a new representation of hyperspectral data representation generated by a merging process. Different hyperspectral region models and similarity metrics defining the merging orders are presented and analyzed. The resulting merging sequence is stored in a BPT structure which enables image regions to be represented at different resolution levels. The segmentation is performed through an intelligent pruning of the BPT, that selects regions to form the final partition. Experimental results on two hyperspectral data sets have allowed us to compare different merging orders and pruning strategies demonstrating the encouraging performances of BPT-based representation.
Classification of basic roof types based on VHR optical data and digital elevation model

In the frame of seismic vulnerability assessment in urban areas, it is very important to estimate the nature of the roof of every building and, in particular, to make the difference between flat roofs and gable ones. In order to perform this tedious task automatically on a large scale, remote sensing data provide a useful solution. In this study, we use simultaneously very high resolution panchromatic data, and an accurate digital elevation model. The fusion of these two modalities enables the extraction of two mixed features. Based on these features the classification between the two considered classes becomes a simple linearly separable problem.
Curvelet Based Contrast Enhancement in Fluoroscopic Sequences

Image guided interventions have seen growing interest in recent years. The use of X-rays for the procedure impels limiting the dose over time. Image sequences obtained thereby exhibit high levels of noise and very low contrasts. Hence, the development of efficient methods to enable optimal visualization of these sequences is crucial. We propose an original denoising method based on the curvelet transform. First, we apply a recursive temporal filter to the curvelet coefficients. As some residual noise remains, a spatial filtering is performed in the second step, which uses a magnitude-based classification and a contextual comparison of curvelet coefficients. This procedure allows to denoise the sequence while preserving low-contrasted structures, but does not improve their contrast. Finally, a third step is carried out to enhance the features of interest. For this, we propose a line enhancement technique in the curvelet domain. Indeed, thin structures are sparsely represented in that domain, allowing a fast and efficient detection. Quantitative and qualitative evaluations performed on synthetic and real low-dose sequences demonstrate that the proposed method enables a 50% dose reduction.
Hyperspectral Image Classification With Independent Component Discriminant Analysis

In this paper, the use of Independent Component (IC) Discriminant Analysis (ICDA) for remote sensing classification is proposed. ICDA is a nonparametric method for discriminant analysis based on the application of a Bayesian classification rule on a signal composed by ICs. The method uses IC Analysis (ICA) to choose a transform matrix so that the transformed components are as independent as possible. When the data are projected in an independent space, the estimates of their multivariate density function can be computed in a much easier way as the product of univariate densities. A nonparametric kernel density estimator is used to compute the density functions of each IC. Finally, the Bayes rule is applied for the classification assignment. In this paper, we investigate the possibility of using ICDA for the classification of hyperspectral images. We study the influence of the algorithm used to enforce independence and of the number of IC retained for the classification, proposing an effective method to estimate the most suitable number. The proposed method is applied to several hyperspectral images, in order to test different data set conditions (urban/agricultural area, size of the training set, and type of sensor). Obtained results are compared with one of the most commonly used classifier of hyperspectral images (support vector machines) and show the comparative effectiveness of the proposed method in terms of accuracy.
A variational pansharpening approach based on reproducible kernel Hilbert space and heaviside function

In this paper, we propose a continuous modeling and sparse optimization based method for the fusion of a panchromatic (PAN) image and a multispectral (MS) image. The proposed model is mainly based on reproducing kernel Hilbert space (RKHS) and approximated Heaviside function (AHF). In addition, we also design an iterative strategy to recover more image details. The final model is a convex one and solved by the designed alternating direction method of multipliers (ADMM) which guarantees the convergence of the proposed method. Experimental results on two real datasets corresponding to different sensors and different resolutions demonstrate the effectiveness of the proposed approach as compared with several state-of-the-art pansharpening approaches.
Global and local Gram-Schmidt methods for hyperspectral pansharpening

Pansharpening algorithms enable to produce synthetic data with high spatial details and spectral diversity by combining a panchromatic image with multispectral or hyperspectral data. In classical approaches the details extracted from the panchromatic image are introduced into the original multichannel image through injection gains, which can be spatially variant on the image. In this paper we analyze several methods for partitioning an image into regions in which the pixels will share the same injection coefficients. Gram-Schmidt pansharpening methods are used as paradigmatic examples for assessing the performance of global and local gain estimation strategies, using hyperspectral data acquired by sensors mounted on one (Earth Observing-1) or multiple (PROBA and Quick-bird) satellite platforms.
Polarimetric sar target decomposition based on nonlinear decorrelation

In remotely sensed Synthetic Aperture Radar (SAR) images, scattering from a target is often the result of a mixture of different scattering mechanisms. Fully polarimetric data offers the possibility to separate and to interpret them. To achieve this task, several target decomposition techniques have been proposed in the literature. In particular, the advantage of classical target decomposition techniques applied to fully polarimetric SAR data is due to the fact that by evaluating the scattering matrix, various scattering mechanisms and target properties can be identified. Aim of this paper is to evaluate a novel approach based on the use of Nonlinear Principal Component Analysis for the target decomposition. In fact, differently from classical target decomposition techniques, the proposed method is based on the decorrelation of the polarimetric SAR data to extract the inherent information content related to the different scattering mechanisms present in the image. An assessment of the effectiveness of the nonlinear principal component analysis method for target decomposition has been carried out by comparing it with the classical decompositions.
A modified time-frequency method for testing wide-sense stationarity

Recently, a time-frequency approach for testing stationarity was proposed. However, this method inefficiently detects nonstationarities of the first-order. Here, we present two contributions that improve the test performance and allow the detection of first-order evolutions. The first one is to use an adequate distance measure. The second is a modification of the method in order to consider the spectral content from the signal itself when computing the distances.
Robust linear unmixing with enhanced sparsity

Spectral unmixing is a central problem in hyperspectral imagery. It is usually assuming a linear mixture model. Solving this inverse problem, however, can be seriously impacted by a wrong estimation of the number of endmembers, a bad estimation of the endmembers themselves, the spectral variability of the endmembers or the presence of nonlinearities. These problems can result in a too large number of retained endmembers. We propose to tackle this problem by introducing a new formulation for robust linear unmixing enhancing sparsity. With a single tuning parameter the optimization leads to a range of behaviors: from the standard linear model (low sparsity) to a hard classification (maximal sparsity : only one endmember is retained per pixel). We solve the proposed new functional using a computationally efficient proximal primal dual method. The experimental study, including both realistic simulated data and real data demonstrates the versatility of the proposed approach.
Using time series to improve endmembers estimation on multispectral images for snow monitoring

We propose to use the temporal coherence of a time series to extract using Vertex Component Analysis (VCA) the suitable set of endmembers for each scene. The reconstruction error computed on the two previous scenes for each date is used to constrain the selection of the set of endmembers produced by VCA. Snow cover estimation is considered as application. We tested different approaches for abundance estimation (FCLSU, SUnSAL, ELMM) over the French Alps from Moderate Resolution Imaging Spectroradiometer (MODIS) images. Results shows a decrease of the false positive rate with the proposed approach.
Marker-based Hierarchical Segmentation and classification approach for hyperspectral imagery

The Hierarchical SEGmentation (HSEG) algorithm, which is a combination of hierarchical step-wise optimization and spectral clustering, has given good performances for hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. First, a map of markers is constructed using classification results. Then, a novel constrained M-HSEG algorithm is applied. The experimental results show that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for hyperspectral image analysis.
Multidimensional SAR data analysis based on binary partition trees and the covariance matrix geometry

In this paper, we propose the use of the Binary Partition Tree (BPT) as a region-based and multi-scale image representation to process multidimensional SAR data, with special emphasis on polarimetric SAR data. We also show that this approach could be extended to other types of remote sensing imaging technologies, such as hyperspatial imagery. The Binary Partition Tree contains a lot of information about the image structure at different detail levels. At the same time, this structure represents a convenient vehicle to exploit both the statistical properties, as well as the geometric properties of the multidimensional SAR data given by the covariance matrix. The BPT construction process and its exploitation for PolSAR and temporal data information estimation is analyzed in this work. In particular, this work focuses on the speckle noise filtering problem and the temporal characterization of the image dynamics. Results with real data are presented to illustrate the capabilities of the BPT processing approach, specially to maintain the spatial resolution and the small details of the image.
Unsupervised endmember extraction: Application to hyperspectral images from Mars

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 end members. The results on hyperspectral image taken by the instrument OMEGA are shown.
Lossless compact histogram representation for multi-component images: application to histogram equalization

The problem of histogram representation is addressed. In the case of multi-component images, this problem is not trivial: the theoretical "naive" required memory space goes exponentially beyond todays technical capacities. To overcome this problem, we present a lossless compact representation of vector histograms. It is based on the use of a space filling curve to index the data space. The application of this representation to vector histogram equalization is then considered.
Contrast and Error-Based Fusion Schemes for Multispectral Image Pansharpening

The pansharpening process has the purpose of building a high-resolution multispectral image by fusing low spatial resolution multispectral and high-resolution panchromatic observations. A very credited method to pursue this goal relies upon the injection of details extracted from the panchromatic image into an upsampled version of the low-resolution multispectral image. In this letter, we compare two different injection methodologies and motivate the superiority of contrast-based methods both by physical consideration and by numerical tests carried out on remotely sensed data acquired by IKONOS and Quickbird sensors.
Fusion of Spectral and Spatial Information for Classification of Hyperspectral Remote-Sensed Imagery by Local Graph

Hyperspectral imagery contains a wealth of spectral and spatial information that can improve target detection and recognition performance. Conventional feature extraction methods cannot fully exploit both spectral and spatial information. Data fusion by simply stacking different feature sources together does not take into account the differences between feature sources. In this paper, a local graph-based fusion (LGF) method is proposed to couple dimension reduction and feature fusion of the spectral information (i.e., the spectra in the HS image) and the spatial information [extracted by morphological profiles (MPs)]. In the proposed method, the fusion graph is built on the full data by moving a sliding window from the first pixel to the last one. This yields a clear improvement over a previous approach with fusion graph built on randomly selected samples. Experimental results on real hyperspectral images are very encouraging. Compared to the methods using only single feature and stacking all the features together, the proposed LGF method improves the overall classification accuracy on one of the data sets for more than 20% and 5%, respectively.
Hyperspectral pansharpening based on unmixing techniques

Pansharpening first aims at fusing a panchromatic image with a multispectral image to generate an image with the high spatial resolution of the former and the spectral resolution of the latter. In the last decade many pansharpening algorithms have been presented in the literature using multispectral data. With the increasing availability of hyperspectral systems these methods are now extending to hyperspectral images. But the problem of the mixed pixel is generally ignored by the existing methods since their goal is to preserve the spectral information and add spatial information. In this work we compare different approaches to deal with mixed pixels as a pre-processing step before doing the fusion. This should improved the result by adding missing spectral information available in the reference image because of the mixed pixel.
A Critical Comparison Among Pansharpening Algorithms

Pansharpening aims at fusing a multispectral and a panchromatic image, featuring the result of the processing with the spectral resolution of the former and the spatial resolution of the latter. In the last decades, many algorithms addressing this task have been presented in the literature. However, the lack of universally recognized evaluation criteria, available image data sets for benchmarking, and standardized implementations of the algorithms makes a thorough evaluation and comparison of the different pansharpening techniques difficult to achieve. In this paper, the authors attempt to fill this gap by providing a critical description and extensive comparisons of some of the main state-of-the-art pansharpening methods. In greater details, several pansharpening algorithms belonging to the component substitution or multiresolution analysis families are considered. Such techniques are evaluated through the two main protocols for the assessment of pansharpening results, i.e., based on the full- and reduced-resolution validations. Five data sets acquired by different satellites allow for a detailed comparison of the algorithms, characterization of their performances with respect to the different instruments, and consistency of the two validation procedures. In addition, the implementation of all the pansharpening techniques considered in this paper and the framework used for running the simulations, comprising the two validation procedures and the main assessment indexes, are collected in a MATLAB toolbox that is made available to the community.
Buried mines detection and classification: advanced technologies and signal processing

In order to improve the mine countermeasures capability, there is a need to investigate new techniques which would enable the detection, localisation and classification of buried mines. This paper deals with results obtained in France, under a sonar program involving GESMA and three academic laboratories. An experimental approach has been preferred. Two techniques are under evaluation: the low frequency synthetic aperture sonar (SAS) mounted on a platform as a side scan sonar dedicated to buried mines detection and a sonar mounted just below a platform, looking vertically at the seabed, dedicated to buried mines classification.
On the use of the Hotelling's T2 statistic for the hierarchical clustering of hyperspectral data

In this work we propose a hierarchical clustering methodology for hyperspectral data based on the Hotelling's T2 statistic. For each hypespectral sample data, the statistical sample mean is calculated using a window-based neighborhood. Then, the pairwise similarities between any two hyperspectral samples are computed based on the Hotelling's T2 statistic. This statistic assumes a Gaussian distribution of the data while hyperspectral data have been observed to be long tailed distributed. In order to improve the statistic robustness we use the Fixed Point estimates, and compare them to the classical sample mean estimator. The similarities are then used to hierarchically cluster the hyperspectral data. We give some preliminary qualitative results of the proposed approach over the Indian Pines hyperspectral scene. Results show that the use of the Fixed Point estimator does not significantly affect the clustering results. Further work will be focused on the use of the robust Hotelling statistic.
Multiple Kernel Learning for Hyperspectral Image Classification: A Review

With the rapid development of spectral imaging techniques, classification of hyperspectral images (HSIs) has attracted great attention in various applications such as land survey and resource monitoring in the field of remote sensing. A key challenge in HSI classification is how to explore effective approaches to fully use the spatial–spectral information provided by the data cube. Multiple kernel learning (MKL) has been successfully applied to HSI classification due to its capacity to handle heterogeneous fusion of both spectral and spatial features. This approach can generate an adaptive kernel as an optimally weighted sum of a few fixed kernels to model a nonlinear data structure. In this way, the difficulty of kernel selection and the limitation of a fixed kernel can be alleviated. Various MKL algorithms have been developed in recent years, such as the general MKL, the subspace MKL, the nonlinear MKL, the sparse MKL, and the ensemble MKL. The goal of this paper is to provide a systematic review of MKL methods, which have been applied to HSI classification. We also analyze and evaluate different MKL algorithms and their respective characteristics in different cases of HSI classification cases. Finally, we discuss the future direction and trends of research in this area.