nils-herrmann/clean_scirepeval huggingface dataset:
Complementarity of Discriminative Classifiers and Spectral Unmixing Techniques for the Interpretation of Hyperspectral Images

Classification and spectral unmixing are two important techniques for hyperspectral data exploitation. Traditionally, these techniques have been exploited independently. In this paper, we propose a new technique that exploits their complementarity. Specifically, we develop a new framework for semisupervised hyperspectral image classification that naturally integrates the information provided by discriminative classification and spectral unmixing. The idea is to assign more confidence to the information provided by discriminative classification for those pixels that can be easily catalogued due to their spectral purity. For those pixels that are more highly mixed in nature, we assign more confidence to the information provided by spectral unmixing. In this case, we use a traditional spectral unmixing chain to produce the abundance fractions of the pure signatures (endmembers) that model the mixture information at a subpixel level. The decision on which source of information is prioritized in the process is taken adaptively, when new unlabeled samples are selected and included in our semisupervised framework. In this regard, the proposed approach can adaptively integrate these two sources of information without the need to establish any weight parameters, thus exploiting the complementarity of classification and unmixing and selecting the most appropriate source of information in each case. In order to test our concept, which has similar computational complexity as traditional semisupervised classification strategies, we have used two different hyperspectral data sets with different characteristics and spatial resolution. In our experiments, we consider two different discriminative classifiers: multinomial logistic regression and probabilistic support vector machine. The obtained results indicate that the proposed approach, which jointly exploits the features provided by classification and spectral unmixing in adaptive fashion, offers an effective solution to improve classification performance in hyperspectral scenes containing mixed pixels.
FPGA implementation of a maximum volume algorithm for endmember extraction from hyperspectral imagery

Endmember extraction is a very important technique in hyperspectral unmixing. Due to the fact that more applications require real or near real-time processing capabilities, high performance computing based on field-programmable gate array (FPGA) for endmember extraction has received considerable interest in recent years. In this paper, we propose a real-time implementation of maximum volume algorithms (RT-MaxV) using a Kintex-7 FPGA. The proposed RT-MaxV does not need dimensionality reduction like other real-time fast simplex growing algorithms (RT-FSGA). Our experimental results, obtained using the AVIRIS Cuprite data set, indicate that the proposed algorithm has better accuracy and performance than RT-FSGA, and its FPGA implementation achieves real-time processing capability in the considered problem.
Probabilistic-Kernel Collaborative Representation for Spatial–Spectral Hyperspectral Image Classification

This paper presents a new approach for accurate spatial-spectral classification of hyperspectral images, which consists of three main steps. First, a pixelwise classifier, i.e., the probabilistic-kernel collaborative representation classification (PKCRC), is proposed to obtain a set of classification probability maps using the spectral information contained in the original data. This is achieved by means of a kernel extension based on collaborative representation (CR) classification. Then, an adaptive weighted graph (AWG)-based postprocessing model is utilized to include the spatial information by refining the obtained pixelwise probability maps. Furthermore, to deal with scenarios dominated by limited training samples, we modify the postprocessing model by fixing the probabilistic outputs of training samples to integrate the spatial and label information. The proposed approach is able to cover different analysis scenarios by means of a fully adaptive processing chain (based on three steps) for hyperspectral image classification. All the techniques that integrate the proposed approach have a closed-form analytic solution and are easy to be implemented and calculated, exhibiting potential benefits for hyperspectral image classification under different conditions. Specifically, the proposed method is experimentally evaluated using two real hyperspectral imagery data sets, exhibiting good classification performance even when the number of training samples available a priori is very limited.
Validation of spectral unmixing methods using photometry and topography information

In this work the performance of spectral unmixing procedures applied on hyperspectral images of granular mixtures are compared. For that purpose we consider a laboratory image and synthetic images, the latter being created using a original algorithmic process while borrowing some aspects of the real data. The nonlinear effects of light multiple scattering within the mixture can be partially compensated by transformation of the image using the Hapke model under different levels of injected information regarding topography and photometry of the scene. The validation of the different methods and deconvolution processes is established by comparing the results with a “ground truth” through the mean Spectral Angle for the extracted endmembers and the mean Abundances Root Mean Square Error for the estimated abundances. Interpretation of the results indicates that is safer to extract the endmembers from the original version of image unless topography and most importantly photometry are precisely known. On the other hand better distribution maps are obtained in general from the transformed version of the image.
A New Hybrid Strategy Combining Semisupervised Classification and Unmixing of Hyperspectral Data

Spectral unmixing and classification have been widely used in the recent literature to analyze remotely sensed hyperspectral data. However, few strategies have combined these two approaches in the analysis. In this work, we propose a new hybrid strategy for semisupervised classification of hyperspectral data which exploits both spectral unmixing and classification in a synergetic fashion. During the process, the most informative unlabeled samples are automatically selected from the pool of candidates, thus reducing the computational cost of the process by including only the most informative unlabeled samples. Our approach integrates a well-established discriminative probabilistic classifier-the multinomial logistic regression (MLR) with different spectral unmixing chains, thus bridging the gap between spectral unmixing and classification and exploiting them together for the analysis of hyperspectral data. The effectiveness of the proposed method is evaluated using two real hyperspectral data sets, collected by the NASA Jet Propulsion Laboratory's airborne visible infrared imaging spectrometer (AVIRIS) over the Indian Pines region, Indiana, and by the reflective optics spectrographic imaging system (ROSIS) over the University of Pavia, Italy.
H-COMP: a tool for quantitative and comparative analysis of endmember identification algorithms

Over the past years, several endmember extraction algorithms have been developed for spectral mixture analysis of hyperspectral data. Due to a lack of quantitative approaches to substantiate new algorithms, available methods have not been rigorously compared using a unified scheme. In this paper, we describe H-COMP, an IDL (Interactive Data Language)-based software toolkit for visualization and interactive analysis of results provided by endmember selection methods. The suitability of using H-COMP for assessment and comparison of endmember extraction algorithms is demonstrated in this work by a comparative analysis of three standard algorithms: Pixel Purity Index (PPI), N-FINDR, and Automated Morphological Endmember Extraction (AMEE). Simulated and real hyperspectral datasets, collected by the NASA/JPL Airborne Visible-Infrared Imaging Spectrometer (AVIRIS), are used to carry out a comparative effort, focused on the definition of reliable endmember quality metrics.
Improving the scalability of parallel algorithms for hyperspectral image analysis using adaptive message compression

In previous work, we have reported that the scalability of parallel processing algorithms for hyperspectral image analysis is affected by the amount of data to exchanged through the communication network of the parallel system. However, large messages are common in hyperspectral imaging applications since processing algorithms are often pixel-based, and each pixel vector to be exchanged through the communication network is made up of hundreds of spectral values. Thus, decreasing the amount of data to be exchanged could improve the scalability and parallel performance. In this paper, we propose a new framework based on intelligent utilization of data compression techniques for improving the scalability of a standard spectral unmixin-based parallel hyperspectral processing chain on heterogeneous networks of workstations. Our experimental results indicate that adaptive, wavelet-based lossy compression can lead to improvements in the scalability of the parallel algorithms without significantly sacrificing algorithm analysis accuracy.
Parallel Morphological/Neural Classification of Remote Sensing Images Using Fully Heterogeneous and Homogeneous Commodity Clusters

The wealth spatial and spectral information available from last-generation Earth observation instruments has introduced extremely high computational requirements in many applications. Most currently available parallel techniques treat remotely sensed data not as images, but as unordered listings of spectral measurements with no spatial arrangement. In thematic classification applications, however, the integration of spatial and spectral information can be greatly beneficial. Although such integrated approaches can be efficiently mapped in homogeneous commodity clusters, low-cost heterogeneous networks of computers (HNOCs) have soon become a standard tool of choice in Earth and planetary missions. In this paper, we develop a new morphological/neural parallel algorithm for commodity cluster-based analysis of high-dimensional remotely sensed image data sets. The algorithms accuracy and parallel performance are tested (in the context of a real precision agriculture application) using two parallel platforms: a fully heterogeneous cluster made up of 16 workstations at University of Maryland, and a massively parallel Beowulf cluster at NASA's Goddard Space Flight Center
Multicore Real-Time Implementation of a Full Hyperspectral Unmixing Chain

Solving the mixture problem in remotely sensed hyperspectral images remains a challenging task. In particular, solutions are needed in order to obtain a response for applications with real-time constraints. In the last decade, several efforts have been developed, many of them using graphics processing units (GPUs) and focused on the exploitation of spectral information alone. However, a few spectral unmixing chains have been developed using other architectures such as multicore processors, field programmable gate arrays, or Intel Xeon Phi coprocessors. In this letter, we develop a new parallel unmixing chain for multicore processors. Compared with other approaches, the proposed spatial–spectral alternative takes advantage of the complementary information provided by the spatial correlation of the pixels in the image in addition to the spectral information. Our implementation has been optimized using the application program interface OpenMP and the Intel Math Kernel Library on two multicore architectures, and using real analysis scenarios. The results reveal competitive real-time performance compared with another compute unified device architecture implementation previously developed for GPUs.
New Postprocessing Methods for Remote Sensing Image Classification: A Systematic Study

This paper develops several new strategies for remote sensing image classification postprocessing (CPP) and conducts a systematic study in this area. CPP is defined as a refinement of the labeling in a classified image in order to enhance its original classification accuracy. The current mainstream classification methods (preprocessing) extract additional spatial features in order to complement spectral information and enhance classification using spectral responses alone. On the other hand, however, the CPP methods, providing a new solution to improve classification accuracy by refining the initial result, have not received sufficient attention. They have potential for achieving comparable accuracy to the preprocessing methods but in a more direct and succinct way. In this paper, we consider four groups of CPP strategies: filtering; random field; object-based voting; and relearning. In addition to the state-of-the-art CPP algorithms, we also propose a series of new ones, e.g., anisotropic probability diffusion and primitive cooccurrence matrix. In experiments, a number of multisource remote sensing data sets are used for evaluation of the considered CPP algorithms. It is shown that all the CPP strategies are capable of providing more accurate results than the raw classification. Among them, the relearning approaches achieve the best results. In addition, our relearning algorithms are compared with the state-of-the-art spectral-spatial classification. The results obtained further verify the effectiveness of CPP in different remote sensing applications.
Onboard payload-data dimensionality reduction

The finer spatial, spectral and radiometric resolutions of current and planned sensors are rendering increasingly-high data rates which, coupled with limited on-board storage, downlink bandwidth and receiving ground station availability, make high-throughput, high-performance data-reduction techniques essential in forthcoming missions. On this paper we describe an algorithm well suited to high-dimensional data as those produced by multispectral and hyperspectral sensors, both highly relevant in a broad range of Earth Observation activities with the latter becoming increasingly available and delivering the highest data rates. The performance of parallel implementations of the algorithm on multi-core and GPU architectures is also evaluated.
A Novel Semi-Supervised Method for Obtaining Finer Resolution Urban Extents Exploiting Coarser Resolution Maps

In this work, we present a new semi-supervised strategy for obtaining finer spatial resolution urban maps from coarser resolution satellite data. Our method first uses a coarse resolution map as a source of training data. Then, we use semi-supervised learning in order to refine the set of initial (labeled) training samples by the inclusion of additional (reliable) unlabeled samples at the finer resolution level, in fully automatic fashion. The new unlabeled samples are automatically generated by our proposed methodology, which only requires a limited number of initial labeled samples for initialization purposes. Then, we conduct land cover classification (at the finer spatial resolution level) using a probabilistic multinomial logistic regression (MLR) classifier-in both supervised and semi-supervised fashion-by considering different numbers of labeled and unlabeled samples. In order to exploit spatial information, we use a Markov random field (MRF)-based postprocessing strategy to refine the obtained classification results. In order to test our concept, we use a global dataset: the European Space Agency's GlobCover product, as the coarser resolution map (300-m spatial resolution). Our experimental evaluation is further conducted using Landsat data (30-m spatial resolution) collected over three different locations in the city of Sao Paulo, Brazil, and over two different locations in the city of Guangzhou, China. We obtain promising results in the generation of finer resolution urban extent maps using very limited training samples, derived in all cases from the GlobCover product. These experiments suggest the potential of GlobCover to provide reliable training data in order to support mapping of urban areas at a global scale.
A fast iterative algorithm for implementation of pixel purity index

The pixel purity index (PPI) has been widely used in hyperspectral image analysis for endmember extraction due to its publicity and availability in the Environment for Visualizing Images (ENVI) software. Unfortunately, its detailed implementation has never been made available in the literature. This paper investigates the PPI based on limited published results and proposes a fast iterative algorithm to implement the PPI, referred to as fast iterative PPI (FIPPI). It improves the PPI in several aspects. Instead of using randomly generated vectors as initial endmembers, the FIPPI produces an appropriate initial set of endmembers to speed up its process. Additionally, it estimates the number of endmembers required to be generated by a recently developed concept, virtual dimensionality (VD) which is one of the most crucial issues in the implementation of PPI. Furthermore, it is an iterative algorithm, where an iterative rule is developed to improve each of the iterations until it reaches a final set of endmembers. Most importantly, it is an unsupervised algorithm as opposed to the PPI, which requires human intervention to manually select a final set of endmembers. The experiments show that both the FIPPI and the PPI produce very close results, but the FIPPI converges very rapidly with significant savings in computation.
Subspace-Based Support Vector Machines for Hyperspectral Image Classification

Hyperspectral image classification has been a very active area of research in recent years. It faces challenges related with the high dimensionality of the data and the limited availability of training samples. In order to address these issues, subspace-based approaches have been developed to reduce the dimensionality of the input space in order to better exploit the (limited) training samples available. An example of this strategy is a recently developed subspace-projection-based multinomial logistic regression technique able to characterize mixed pixels, which are also an important concern in the analysis of hyperspectral data. In this letter, we extend the subspace-projection-based concept to support vector machines (SVMs), a very popular technique for remote sensing image classification. For that purpose, we construct the SVM nonlinear functions using the subspaces associated to each class. The resulting approach, called SVMsub, is experimentally validated using a real hyperspectral data set collected using the National Aeronautics and Space Administration's Airborne Visible/Infrared Imaging Spectrometer. The obtained results indicate that the proposed algorithm exhibits good performance in the presence of very limited training samples.
Multi-GPU Implementation of the Minimum Volume Simplex Analysis Algorithm for Hyperspectral Unmixing

Spectral unmixing is an important task in remotely sensed hyperspectral data exploitation. The linear mixture model has been widely used to unmix hyperspectral images by identifying a set of pure spectral signatures, called endmembers, and estimating their respective abundances in each pixel of the scene. Several algorithms have been proposed in the recent literature to automatically identify endmembers, even if the original hyperspectral scene does not contain any pure signatures. A popular strategy for endmember identification in highly mixed hyperspectral scenes has been the minimum volume simplex analysis (MVSA), known to be a computationally very expensive algorithm. This algorithm calculates the minimum volume enclosing simplex, as opposed to other algorithms that perform maximum simplex volume analysis (MSVA). The high computational complexity of MVSA, together with its very high memory requirements, has limited its adoption in the hyperspectral imaging community. In this paper, we develop several optimizations to the MVSA algorithm. The main computational task of MVSA is the solution of a quadratic optimization problem with equality and inequality constraints, with the inequality constraints being in the order of the number of pixels multiplied by the number of endmembers. As a result, storing and computing the inequality constraint matrix is highly inefficient. The first optimization presented in this paper uses algebra operations in order to reduce the memory requirements of the algorithm. In the second optimization, we use graphics processing units (GPUs) to effectively solve (in parallel) the quadratic optimization problem involved in the computation of MVSA. In the third optimization, we extend the single GPU implementation to a multi-GPU one, developing a hybrid strategy that distributes the computation while taking advantage of GPU accelerators at each node. The presented optimizations are tested in different analysis scenarios (using both synthetic and real hyperspectral data) and shown to provide state-of-the-art results from the viewpoint of unmixing accuracy and computational performance. The speedup achieved using the full GPU cluster compared to the CPU implementation in tenfold in a real hyperspectral image.
Morphological feature extraction for automatic registration of multispectral images

The task of image registration can be divided into two major components, i.e., the extraction of control points or features from images, and the search among the extracted features for the matching pairs that represent the same feature in the images to be matched. Manual extraction of control features can be subjective and extremely time consuming, and often results in few usable points. On the other hand, automated feature extraction allows using invariant target features such as edges, corners, and line intersections as relevant landmarks for registration purposes. In this paper, we present an extension of a recently developed morphological approach for automatic extraction of landmark chips and corresponding windows in a fully unsupervised manner for the registration of multispectral images. Once a set of chip-window pairs is obtained, a (hierarchical) robust feature matching procedure, based on a multiresolution overcomplete wavelet decomposition scheme, is used for registration purposes. The proposed method is validated on a pair of remotely sensed scenes acquired by the Advanced Land Imager (ALI) multispectral instrument and the Hyperion hyperspectral instrument aboard NASA's Earth Observing-1 satellite.
On the incorporation of spatial information to endmember identification algorithms without the pure pixel assumption

Spectral unmixing is a commonly used technique in hyperspectral data exploitation. It expresses the measured spectral signature of each pixel in the data as a combination of spectrally pure constituent spectra, called endmembers, and a set of correspondent fractions, or abundances that indicate the proportion of each endmember present in the mixture. Over the last years, several algorithms have been developed for automatic endmember identification by assuming the presence of at least one pure spectral signature for each distinct material. However, this assumption often does not hold in practice due to spatial resolution, mixing phenomena, and other considerations. In this paper, we investigate if spatial information can assist the endmember searching process conducted by algorithms that do not assume the presence of pure pixels in the hyperspectral data. For this purpose, we use recently developed spatial pre-processing techniques that do not require modifications in the subsequent endmember identification process, conducted in this work using minimum volume enclosing algorithms. Our experimental results, conducted using both simulated and real hyperspectral data sets, reveal that spatial information can be beneficial to guide the endmember identification process when pure pixels are not assumed to be present in the hyperspectral data.