nils-herrmann/clean_scirepeval huggingface dataset:
Spectral–Spatial Classification of Multispectral Images Using Kernel Feature Space Representation

Over the last few years, several new strategies have been proposed for spectral-spatial classification of remotely sensed image data, for cases when high spatial and spectral resolutions are available. In this letter, we focus on the possibility of performing advanced spectral-spatial classification of remote sensing images with limited spectral resolution (often called multispectral). A new strategy is proposed, where the spectral dimensionality of the multispectral data is first expanded by using nonlinear feature extraction with kernel methods such as kernel principal component analysis. Then, extended multiattribute profiles (EMAPs), built on the expanded set of spectral features, are used to include spatial information. This strategy allows us to first decompose different spectral clusters into different spectral features and further improve the spatial discrimination. The resulting EMAPs are used for classification using advanced classifiers such as support vector machines and random forests. We test our proposed methodology with different multispectral data sets obtaining state-of-the-art classification results.
Unmixing with SLIC superpixels for hyperspectral change detection

Change detection by unmixing has been shown to provide enhanced change detection performance for hyperspectral images with respect to more traditional approaches, especially when the temporal images contain sub-pixel level changes. In a recent paper, change detection by spectral unmixing was investigated in detail and the advantages that can be gained by using such an approach were systematically presented through various experimental studies. However, the utilized unmixing-based change detection approach relied solely on spectral information and disregarded the spatial distribution in the scene, which inevitably limits the performance that can be achieved. In this paper, superpixels are used to integrate the spatial information in the image into the unmixing process, which in turn enhances the change detection performance with respect to spectral unmixing based change detection.
Estimating Nonlinearities in p-Linear Hyperspectral Mixtures

Accurately estimating the elements in Earth observations is crucial when assessing specific features such as air quality index, water pollution, or urbanization process behavior. Moreover, physical–chemical composition can be retrieved from hyperspectral images when proper spectral unmixing architectures are employed. Specifically, when linear and nonlinear combinations of endmembers (pure spectral components) are accurately characterized, hyperspectral unmixing plays a key role in understanding and quantifying phenomena occurring over the instantaneous field-of-view. Thus, reliable detection of nonlinear reflectance behavior can play a key role in enhancing hyperspectral unmixing performance. In this paper, two new methods for adaptive design of mixture models for hyperspectral unmixing are introduced. One of the methods relies on exploiting geometrical features of hyperspectral signatures in terms of nonorthogonal projections onto the space induced by the endmembers’ spectra. Then, an iterative process aims at understanding the order of local nonlinearity that is displayed by each endmember over every pixel. An improved version of an artificial neural network-based approach for nonlinearity order information is also considered and compared. Experimental results show that the proposed approaches are actually able to retrieve thorough information on the nature of the nonlinear effects over the image, while providing excellent performance in reconstructing the given data sets.
Parallel and Distributed Dimensionality Reduction of Hyperspectral Data on Cloud Computing Architectures

Cloud computing offers the possibility to store and process massive amounts of remotely sensed hyperspectral data in a distributed way. Dimensionality reduction is an important task in hyperspectral imaging, as hyperspectral data often contains redundancy that can be removed prior to analysis of the data in repositories. In this regard, the development of dimensionality reduction techniques in cloud computing environments can provide both efficient storage and preprocessing of the data. In this paper, we develop a parallel and distributed implementation of a widely used technique for hyperspectral dimensionality reduction: principal component analysis (PCA), based on cloud computing architectures. Our implementation utilizes Hadoop's distributed file system (HDFS) to realize distributed storage, uses Apache Spark as the computing engine, and is developed based on the map-reduce parallel model, taking full advantage of the high throughput access and high performance distributed computing capabilities of cloud computing environments. We first optimized the traditional PCA algorithm to be well suited for parallel and distributed computing, and then we implemented it on a real cloud computing architecture. Our experimental results, conducted using several hyperspectral datasets, reveal very high performance for the proposed distributed parallel method.
Sparse Unmixing With Dictionary Pruning for Hyperspectral Change Detection

The localization of changes that occur between the images in a multitemporal series is crucial for many applications, ranging from environmental monitoring to military surveillance. In contrast to traditional change detection methods, unmixing-based change detection has been shown to have the important added benefit of providing subpixel-level information on the nature of the changes, instead of only providing the location of the changes. Recently, sparse unmixing has also been introduced to hyperspectral change detection, resulting in a method that circumvents the drawbacks of regular spectral unmixing approaches. Sparse unmixing-based change detection reveals the changes that occur in a multitemporal series, at subpixel level, and in terms of the library spectra and their sparse abundances, and provides enhanced change detection performance, especially when subpixel-level changes have occurred. However, sparse unmixing is generally an ill-conditioned and time-consuming process, especially as the size of the utilized spectral library increases. In this paper, dictionary pruning is exploited for the first time for hyperspectral change detection using sparse unmixing, in order to alleviate the ill-conditioning of the problem and achieve decreased computation times and enhanced change detection performance. Experimental results on both realistic synthetic and real datasets are used to validate the proposed approach.
Endmember extraction from hyperspectral imagery using a parallel ensemble approach with consensus analysis

We have explored in this paper a framework to test in a quantitative manner the stability of different endmember extraction and spectral unmixing algorithms based on the concept of Consensus Clustering. The idea is to investigate if the sensibility of those algorithms to the number of endmembers can be used to estimate this parameter itself. Preliminary results on synthetic data reveal that the proposed scheme, which can be implemented efficiently in parallel, can compete with state-of-the-art schemes.
Spatial-spectral endmember extraction from remotely sensed hyperspectral images using the watershed transformation

In this paper, we investigate the use of the watershed transformation for integrating spatial and spectral information in the process of endmember extraction for spectral unmixing of hyperspectral images. The proposed approach is presented as a preprocessing module designed to automatically select a small subset of pixels containing potentially relevant candidates from both spatial and spectral point of view. Dimensionality reduction is required. The idea is to use the morphological watershed transformation to guide the endmember searching process to spatially homogeneous and spectrally “purer” areas. Here the main assumption is that such areas can be located at the local minima of the catchment basins, and far away from watershed lines that define the transition areas between different regions, expected to contain mixed pixels. Experimental results, conducted using a database of 28 simulated hyperspectral data sets obtained through manipulation of a real hyperspectral image acquired by the Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over a mixed scenario including agricultural, vegetation, and urban areas, suggests a promising trade-off between percentage of endmember candidates retained and degree of spectral purity of predominant endmembers.
A Quantitative and Comparative Assessment of Unmixing-Based Feature Extraction Techniques for Hyperspectral Image Classification

Over the last years, many feature extraction techniques have been integrated in processing chains intended for hyperspectral image classification. In the context of supervised classification, it has been shown that the good generalization capability of machine learning techniques such as the support vector machine (SVM) can still be enhanced by an adequate extraction of features prior to classification, thus mitigating the curse of dimensionality introduced by the Hughes effect. Recently, a new strategy for feature extraction prior to classification based on spectral unmixing concepts has been introduced. This strategy has shown success when the spatial resolution of the hyperspectral image is not enough to separate different spectral constituents at a sub-pixel level. Another advantage over statistical transformations such as principal component analysis (PCA) or the minimum noise fraction (MNF) is that unmixing-based features are physically meaningful since they can be interpreted as the abundance of spectral constituents. In turn, previously developed unmixing-based feature extraction chains do not include spatial information. In this paper, two new contributions are proposed. First, we develop a new unmixing-based feature extraction technique which integrates the spatial and the spectral information using a combination of unsupervised clustering and partial spectral unmixing. Second, we conduct a quantitative and comparative assessment of unmixing-based versus traditional (supervised and unsupervised) feature extraction techniques in the context of hyperspectral image classification. Our study, conducted using a variety of hyperspectral scenes collected by different instruments, provides practical observations regarding the utility and type of feature extraction techniques needed for different classification scenarios.
Efficient Implementation of Hyperspectral Anomaly Detection Techniques on GPUs and Multicore Processors

Anomaly detection is an important task for hyperspectral data exploitation. Although many algorithms have been developed for this purpose in recent years, due to the large dimensionality of hyperspectral image data, fast anomaly detection remains a challenging task. In this work, we exploit the computational power of commodity graphics processing units (GPUs) and multicore processors to obtain implementations of a well-known anomaly detection algorithm developed by Reed and Xiaoli (RX algorithm), and a local (LRX) variant, which basically consists in applying the same concept to a local sliding window centered around each image pixel. LRX has been shown to be more accurate to detect small anomalies but computationally more expensive than RX. Our interest is focused on improving the computational aspects, not only through efficient parallel implementations, but also by analyzing the mathematical issues of the method and adopting computationally inexpensive solvers. Futhermore, we also assess the energy consumption of the newly developed parallel implementations, which is very important in practice. Our optimizations (based on software and hardware techniques) result in a significant reduction of execution time and energy consumption, which are keys to increase the practical interest of the considered algorithms. Indeed, for RX, the runtime obtained is less than the data acquisition time when real hyperspectral images are used. Our experimental results also indicate that the proposed optimizations and the parallelization techniques can significantly improve the general performance of the RX and LRX algorithms while retaining their anomaly detection accuracy.
A Quantitative and Comparative Analysis of Different Implementations of N-FINDR: A Fast Endmember Extraction Algorithm

The N-FINDR algorithm is one of the most widely used and successfully applied methods for automatically determining endmembers in hyperspectral image data without using a priori information. The algorithm attempts to automatically find the simplex of maximum volume that can be inscribed within the hyperspectral data set. Due to the intrinsic complexity of remotely sensed scenes, the final volume-based solution provided by N-FINDR may be not the global maximum. In addition, the final results provided by the algorithm are typically dependent of its initialization. In this letter, we explore the aforementioned issues and conduct a quantitative and comparative analysis of different (available and new) strategies for the implementation of N-FINDR. Our experimental evaluation and comparison are conducted using two well-known hyperspectral scenes collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible Infrared Imaging Spectrometer.
Parallel Implementation of Spatial–Spectral Endmember Extraction on Graphic Processing Units

The identification of pure spectral signatures (endmembers) in remotely sensed hyperspectral images has traditionally focused on the spectral information alone. Recently, techniques such as the spatial–spectral endmember extraction (SSEE) have incorporated both the spectral and the spatial information contained in the scene. Since hyperspectral images contain very detailed information in the spatial and spectral domain, the integration of these two sources of information generally comes with a significant increase in computational complexity. In this paper, we develop a new computationally efficient implementation of SSEE using commodity graphics processing units (GPUs). The relevance of GPUs comes from their very low cost, compact size, and the possibility to obtain significant acceleration factors by exploiting properly the GPU hardware architecture. Our experimental results, focused on evaluating the candidate endmembers produced by SSEE and also the computational performance of the GPU implementation, indicated significant acceleration factors that allow exploiting the SSEE method in computationally efficient fashion.
Analysis of different strategies for incorporating spatial information in the design of endmember extraction algorithms from hyperspectral data

Over the last decade, several algorithms have been developed for automatic or semi-automatic extraction of spectral endmembers from hyperspectral image data. In this paper, we present a thorough analytical comparison of endmember extraction methods which include spatial information in the search of spectral endmembers versus a few algorithms which are exclusively based on spectral information. Our quantitative and comparative assessment of algorithm accuracy and computational performance, conducted using both synthetic and real hyperspectral data, provides interesting findings about the potential benefits that can be obtained after incorporating spatial information into the design of endmember extraction algorithms.
Normal Endmember Spectral Unmixing Method for Hyperspectral Imagery

The normal compositional model (NCM) has been introduced to characterize mixed pixels in hyperspectral images, particularly when endmember variability needs to be considered in the unmixing process. Each pixel is modeled as a linear combination of endmembers, which are treated as Gaussian random variables in order to capture such spectral variability. Since the combination coefficients (i.e., abundances) and the endmembers are unknown variables at the same time in the NCM, the parameter estimation is more difficult in comparison with conventional approaches. In order to address this issue, we propose a new Bayesian method, termed normal endmember spectral unmixing (NESU), for improved parameter estimation in this context. It considers the endmembers as known variables (resulting from the extraction of endmember bundles), then performs optimal estimations of the remaining unknown parameters, i.e., the abundances, using Bayesian inference. The particle swarm optimization (PSO) technique is adopted to estimate the optimal values of abundances according to their posterior probabilities. The performance of the proposed algorithm is evaluated using both synthetic and real hyperspectral data. The obtained results demonstrate that the proposed method leads to significant improvements in terms of unmixing accuracies.
A New Genetic Method for Subpixel Mapping Using Hyperspectral Images

Subpixel mapping techniques aim to obtain the spatial location and distribution of subpixels by transforming the information coming from a set of input abundance maps into a classification result with higher spatial resolution. However, traditional subpixel mapping algorithms generally ignore the possible errors that are due to abundance estimation inaccuracies by spectral unmixing techniques. In this paper, we propose a new genetic algorithm-based subpixel mapping technique that solves the subpixel mapping problem by correcting the potential errors in the estimated abundance fractions used as input to the subpixel mapping process. The proposed algorithm has been compared with other two genetic subpixel mapping methods, using both synthetic and real hyperspectral images. Our experimental results demonstrate that the proposed approach outperforms traditional subpixel mapping algorithms, thus providing an effective option to improve the accuracy of subpixel mapping for remotely sensed hyperspectral images.
Spectral-textural endmember extraction

Several available techniques for endmember extraction and spectral unmixing use only the spectral information contained in the hyperspectral data. In this paper, we introduce a novel method for spatial-spectral endmember extraction which incorporates texture features in the quantification of spatial information (jointly with spectral information). Experimental results with simulated and real hyperspectraldata sets indicate that textural information could assist the extraction of spectral endmembers, although a challenging issue still remains: how to combine the final set of endmember candidates (obtained by merging the individual sets of candidates found using spectral, textural and joint spectral-textural information) in order to provide a relevant final solution.
Hyperspectral Unmixing on Multicore DSPs: Trading Off Performance for Energy

Wider coverage of observation missions will increase onboard power restrictions while, at the same time, pose higher demands from the perspective of processing time, thus asking for the exploration of novel high-performance and low-power processing architectures. In this paper, we analyze the acceleration of spectral unmixing, a key technique to process hyperspectral images, on multicore architectures. To meet onboard processing restrictions, we employ a low-power Digital Signal Processor (DSP), comparing processing time and energy consumption with those of a representative set of commodity architectures. We demonstrate that DSPs offer a fair balance between ease of programming, performance, and energy consumption, resulting in a highly appealing platform to meet the restrictions of current missions if onboard processing is required.
Using Linear Spectral Unmixing for Subpixel Mapping of Hyperspectral Imagery: A Quantitative Assessment

Subpixel mapping techniques have been widely utilized to determine the spatial distribution of the different land-cover classes in mixed pixels at a subpixel scale by converting low-resolution fractional abundance maps (estimated by a linear mixture model) into a finer classification map. Over the past decades, many subpixel mapping algorithms have been proposed to tackle this problem. It has been obvious that the utilized abundance map has a strong impact on the subsequent subpixel mapping procedure. However, limited attention has been given to the impact of the different aspects in the spectral unmixing model on the subpixel mapping performance. In this paper, a detailed quantitative assessment of different aspects in linear spectral mixture analysis, such as the criteria used to determine the types of pixels, the abundance sum-to-one constraint in the unmixing, and the accuracy of the utilized abundance maps, is investigated. This is accomplished by designing an experimental procedure with replaceable components. A total of six hyperspectral images (four synthetic and two real) were utilized in our experiments. By investigating these critical issues, we can further improve the performance of subpixel mapping techniques.
Parallel implementation of endmember extraction algorithms using NVidia graphical processing units

Spectral mixture analysis is an important task for remotely sensed hyperspectral data interpretation. In spectral unmixing, both the determination of spectrally pure signatures (endmembers) and the unmixing process that interprets mixed pixels as combinations of endmembers are computationally expensive procedures. An exciting recent development in the field of commodity computing is the emergence of programmable graphics processing units (GPUs), which are now increasingly being used address the ever-growing computational requirements introduced by hyperspectral imaging applications. In this paper, we develop three new GPU-based implementations of endmember extraction algorithms: the pixel purity index (PPI), a kernel version of the PPI (KPPI), and the automatic morphological endmember extraction (AMEE) algorithm. We also provide a GPU-based implementation of the fully constrained linear spectral unmixing algorithm.
Side Information and Noise Learning for Distributed Video Coding Using Optical Flow and Clustering

Distributed video coding (DVC) is a coding paradigm that exploits the source statistics at the decoder side to reduce the complexity at the encoder. The coding efficiency of DVC critically depends on the quality of side information generation and accuracy of noise modeling. This paper considers transform domain Wyner-Ziv (TDWZ) coding and proposes using optical flow to improve side information generation and clustering to improve the noise modeling. The optical flow technique is exploited at the decoder side to compensate for weaknesses of block-based methods, when using motion-compensation to generate side information frames. Clustering is introduced to capture cross band correlation and increase local adaptivity in the noise modeling. This paper also proposes techniques to learn from previously decoded WZ frames. Different techniques are combined by calculating a number of candidate soft side information for low density parity check accumulate decoding. The proposed decoder side techniques for side information and noise learning (SING) are integrated in a TDWZ scheme. On test sequences, the proposed SING codec robustly improves the coding efficiency of TDWZ DVC. For WZ frames using a GOP size of 2, up to 4-dB improvement or an average (Bjøntegaard) bit-rate savings of 37% is achieved compared with DISCOVER.