Hyperspectral Endmember Extraction Research Articles

A Mutation Operator Accelerated Quantum-Behaved Particle Swarm Optimization Algorithm for Hyperspectral Endmember Extraction

The endmember extraction algorithm, which selects a collection of pure signature spectra for different materials, plays an important role in hyperspectral unmixing. In this paper, the endmember extraction algorithm is described as a combinatorial optimization problem and a novel Mutation Operator Accelerated Quantum-Behaved Particle Swarm Optimization (MOAQPSO) algorithm is proposed. The proposed approach employs Quantum-Behaved Particle Swarm Optimization (QPSO) to find endmembers with good performances. To the best of our knowledge, this is the first time that QPSO has been introduced into hyperspectral endmember extraction. In order to follow the law of particle movement, a high-dimensional particle definition is proposed. In addition, in order to avoid falling into a local optimum, a mutation operation is used to increase the population diversity. The proposed MOAQPSO algorithm was evaluated on both synthetic and real hyperspectral data sets. The experimental results indicated that the proposed method obtained better results than other state-of-the-art algorithms, including Vertex Component Analysis (VCA), N-FINDR, and Discrete Particle Swarm Optimization (D-PSO).

Improved discrete swarm intelligence algorithms for endmember extraction from hyperspectral remote sensing images

Endmember extraction is a key step in hyperspectral unmixing. A new endmember extraction framework is proposed for hyperspectral endmember extraction. The proposed approach is based on the swarm intelligence (SI) algorithm, where discretization is used to solve the SI algorithm because pixels in a hyperspectral image are naturally defined within a discrete space. Moreover, a “distance” factor is introduced into the objective function to limit the endmember numbers which is generally limited in real scenarios, while traditional SI algorithms likely produce superabundant spectral signatures, which generally belong to the same classes. Three endmember extraction methods are proposed based on the artificial bee colony, ant colony optimization, and particle swarm optimization algorithms. Experiments with both simulated and real hyperspectral images indicate that the proposed framework can improve the accuracy of endmember extraction.

Extraction of Endmembers From Hyperspectral Images Using A Weighted Fuzzy Purified-Means Clustering Model

Hyperspectral endmembers are the spectra of pure materials that are responsible for generating the mixed pixels in hyperspectral images (HSIs). Hyperspectral endmember extraction (HEE) is essentially an inverse problem, where the unknown endmembers are inferred from the spectral measurements. Efficient extraction of endmembers in HSI relies on a well-defined generative model that captures key factors in HSI generation process, such as the clustering effect in the spatial domain and the noise heterogeneity effect in the spectral domain. This paper presents a weighted fuzzy purified-means (WFP-means) clustering model for HEE, where the endmembers are modeled as mean vectors of individual classes, and the fractional contributions of individual endmembers, called abundances, are treated as soft class membership. Accordingly, an endmember is estimated as the weighted mean of purified pixels in HSI, while the abundances are estimated as the nonnegative regression coefficients. In contrast to a mixed pixel that consists of multiple endmembers, a “purified pixel” is due to a single endmember. The introduction of the concept of “purified pixels” into the fuzzy clustering model leads to an elegant optimization scheme. Moreover, the proposed model accounts for the noise variance heterogeneity issue, which is essential for achieving unbiased abundance estimation. The proposed method is tested on both simulated and real HSI, in comparison with several other HEE methods. The results demonstrate that the proposed method compares favorably with respect to the referenced methods in terms of both endmember and abundance estimation.

The Analysis and Comparison of Hyperspectral Endmember Extraction Algorithms

The Analysis and Comparison of Hyperspectral Endmember Extraction Algorithms

Distributed Parallel Endmember Extraction of Hyperspectral Data Based on Spark

Due to the increasing dimensionality and volume of remotely sensed hyperspectral data, the development of acceleration techniques for massive hyperspectral image analysis approaches is a very important challenge. Cloud computing offers many possibilities of distributed processing of hyperspectral datasets. This paper proposes a novel distributed parallel endmember extraction method based on iterative error analysis that utilizes cloud computing principles to efficiently process massive hyperspectral data. The proposed method takes advantage of technologies including MapReduce programming model, Hadoop Distributed File System (HDFS), and Apache Spark to realize distributed parallel implementation for hyperspectral endmember extraction, which significantly accelerates the computation of hyperspectral processing and provides high throughput access to large hyperspectral data. The experimental results, which are obtained by extracting endmembers of hyperspectral datasets on a cloud computing platform built on a cluster, demonstrate the effectiveness and computational efficiency of the proposed method.

基于最大化N维立体光谱角的高光谱端元提取

基于最大化N维立体光谱角的高光谱端元提取

Fast implementation of linear and nonlinear simplex growing algorithm for hyperspectral endmember extraction

Like any other techniques for hyperspectral image analysis, high performance computing is also the urgent requirement for endmember extraction. In this paper, we investigate the fast implementation of two endmember extraction algorithms named as new simplex growing algorithm (NSGA) and kernel new simplex growing algorithm (KNSGA), which are recently developed as linear and nonlinear alternative to the SGA algorithm, respectively, and shown to be two promising endmember extraction techniques. Due to the fact that when implementing NSGA and KNSGA only the new endmember is processed at a time with the former endmembers staying the same and remaining unchanged, the time cost of simplex volume computation is reduced by simplifying matrix determinant based on the partitioned matrix determinant formula. Experimental results of simulated and real data show that the proposed fast algorithms can greatly reduce computational complexity while their performance remains invariant.

A hyperspectral image endmember extraction algorithm based on generalized morphology

Generalized morphological operator can generate less statistical bias in the output than classical morphological operator. Comprehensive utilization of spectral and spatial information of pixels, an endmember extraction algorithm based on generalized morphology is proposed. For the limitations of morphological operator in the pixel arrangement rule and replacement criteria, the reference pixel is introduced. In order to avoid the cross substitution phenomenon at the boundary of different object categories in the image, an endmember is extracted by calculating the generalized opening-closing (GOC) operator which uses the modified energy function as a distance measure. The algorithm is verified by using simulated data and real data. Experimental results show that the proposed algorithm can extract endmember automatically without prior knowledge and achieve relatively high extraction accuracy.

Kernel simplex growing algorithm for hyperspectral endmember extraction

In order to effectively extract endmembers for hyperspectral imagery where linear mixing model may not be appropriate due to multiple scattering effects, this paper extends the simplex growing algorithm (SGA) to its kernel version. A new simplex volume formula without dimension reduction is used in SGA to form a new simplex growing algorithm (NSGA). The original data are nonlinearly mapped into a high-dimensional space where the scatters can be ignored. To avoid determining complex nonlinear mapping, a kernel function is used to extend the NSGA to kernel NSGA (KNSGA). Experimental results of simulated and real data prove that the proposed KNSGA approach outperforms SGA and NSGA.

A New Preprocessing Technique for Fast Hyperspectral Endmember Extraction

Hyperspectral image processing represents a valuable tool for remote sensing of the Earth. This fact has led to the inclusion of hyperspectral sensors in different airborne and satellite missions for Earth observation. However, one of the main drawbacks encountered when dealing with hyperspectral images is the huge amount of data to be processed, in particular, when advanced analysis techniques such as spectral unmixing are used. The main contribution of this letter is the introduction of a novel preprocessing (PP) module, called SE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> PP, which is based on the integration of spatial and spectral information. The proposed approach can be combined with existing algorithms for endmember extraction, reducing the computational complexity of those algorithms while providing similar figures of accuracy. The key idea behind SE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> PP is to identify and select a reduced set of pixels in the hyperspectral image, so that there is no need to process a large amount of them to get accurate spectral unmixing results. Compared to previous approaches based on similar spatial and spatial-spectral PP strategies, SE <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> PP clearly outperforms their results in terms of accuracy and computation speed, as it is demonstrated with artificial and real hyperspectral images.

Comparison of hyperspectral endmember extraction algorithms

In hyperspectral imagery, endmember extraction is the process of finding a pure spectrum set within the materials present in a hyperspectral scene. However, various endmember extraction algorithms (EEAs) can yield different endmember spectrum sets. This research presents a comparison of four EEAs: pixel purity index, automatic target generation process (ATGP), N-finder (N-FINDR), and simplex identification via split augmented Lagrangian. To perform the comparison, a ground reference geological map is first coregistered with the hyperspectral scene. Then, 12 geological ground truth spectra are chosen. The four EEAs are then used to extract endmember spectra from the scene. Next, the extracted endmember spectra are applied to generate abundance maps using fully constrained least squares. The largest spectrum magnitude in the abundance map is considered the endmember. Finally, the spectral angle mapper and root-mean-square error between the extracted endmember spectrum and the chosen geological spectral spectra are computed, using the angle and minimum error to evaluate similarities. The results of this EEA comparison show that only the ATGP algorithm could consistently identify endmembers with the least computation time. Additionally, the N-FINDR algorithm is able to extract the most endmembers with the closest endmember similarity measures, although this required the highest computation time.

Robust Affine Set Fitting and Fast Simplex Volume Max-Min for Hyperspectral Endmember Extraction

Hyperspectral endmember extraction is to estimate endmember signatures (or material spectra) from the hyperspectral data of an area for analyzing the materials and their composition therein. The presence of noise and outliers in the data poses a serious problem in endmember extraction. In this paper, we handle the noise- and outlier-contaminated data by a two-step approach. We first propose a robust-affine-set-fitting algorithm for joint dimension reduction and outlier removal. The idea is to find a contamination-free data-representative affine set from the corrupted data, while keeping the effects of outliers minimum, in the least squares error sense. Then, we devise two computationally efficient algorithms for extracting endmembers from the outlier-removed data. The two algorithms are established from a simplex volume max-min formulation which is recently proposed to cope with noisy scenarios. A robust algorithm, called worst case alternating volume maximization (WAVMAX), has been previously developed for the simplex volume max-min formulation but is computationally expensive to use. The two new algorithms employ a different kind of decoupled max-min partial optimizations, wherein the design emphasis is on low-complexity implementations. Some computer simulations and real data experiments demonstrate the efficacy, the computational efficiency, and the applicability of the proposed algorithms, in comparison with the WAVMAX algorithm and some benchmark endmember extraction algorithms.

Hyperspectral Data Geometry-Based Estimation of Number of Endmembers Using p-Norm-Based Pure Pixel Identification Algorithm

Hyperspectral endmember extraction is a process to estimate endmember signatures from the hyperspectral observations, in an attempt to study the underlying mineral composition of a landscape. However, estimating the number of endmembers, which is usually assumed to be known a priori in most endmember estimation algorithms (EEAs), still remains a challenging task. In this paper, assuming hyperspectral linear mixing model, we propose a hyperspectral data geometry-based approach for estimating the number of endmembers by utilizing successive endmember estimation strategy of an EEA. The approach is fulfilled by two novel algorithms, namely geometry-based estimation of number of endmembers—convex hull (GENE-CH) algorithm and affine hull (GENE-AH) algorithm. The GENE-CH and GENE-AH algorithms are based on the fact that all the observed pixel vectors lie in the convex hull and affine hull of the endmember signatures, respectively. The proposed GENE algorithms estimate the number of endmembers by using the Neyman–Pearson hypothesis testing over the endmember estimates provided by a successive EEA until the estimate of the number of endmembers is obtained. Since the estimation accuracies of the proposed GENE algorithms depend on the performance of the EEA used, a reliable, reproducible, and successive EEA, called <formula formulatype="inline" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex Notation="TeX">$p$</tex></formula> -norm-based pure pixel identification (TRI-P) algorithm is then proposed. The performance of the proposed TRI-P algorithm, and the estimation accuracies of the GENE algorithms are demonstrated through Monte Carlo simulations. Finally, the proposed GENE and TRI-P algorithms are applied to real AVIRIS hyperspectral data obtained over the Cuprite mining site, Nevada, and some conclusions and future directions are provided.

A Novel Architecture for Hyperspectral Endmember Extraction by Means of the Modified Vertex Component Analysis (MVCA) Algorithm

There is presently a high interest in the spatial industry to develop high-performance on-board processing platforms with a high degree of flexibility, so they can adapt to varying mission needs and/or to future space standards. For this purpose, Field Programmable Gate Array (FPGA) devices have demonstrated to offer an excellent compromise between flexibility and performance. This work presents a novel FPGA-based architecture to be used as part of the hyperspectral linear unmixing processing chain. In particular, this paper introduces a new architecture for hyperspectral endmember extraction accordingly to the Modified Vertex Component Analysis (MVCA) algorithm, which provides a better figure of merit in terms of endmember extraction accuracy versus computational complexity than the Vertex Component Analysis (VCA) algorithm. Two versions of the MVCA algorithm which differ on the use of floating point or integer arithmetic for iteratively projecting the hyperspectral cube onto a direction orthogonal to the subspace spanned by the endmembers already computed have been mapped onto a Xilinx Virtex-5 FPGA. The results demonstrate that both versions are capable of processing hyperspectral images captured by the NASA's AVIRIS sensor in real-time, showing the latter a better performance in terms of hardware resources and processing speed. Furthermore, our proposal constitutes the first published architecture for extracting the endmembers from a hyperspectral image based on the VCA principle and thus, it provides a basis for future FPGA implementations of state-of-the-art hyperspectral algorithms with similar characteristics, such as the Automatic Target Generation Process (ATGP) or the Orthogonal Subspace Projection (OSP) algorithms.

A New Sequential Algorithm for Hyperspectral Endmember Extraction

Endmember extraction is an important step in spectral mixture analysis when endmembers are unknown. Endmembers are usually assumed to be pure pixels present in an image scene. Under this circumstance, endmember extraction is to find the most distinctive pixels. To make the searching process more efficient, the sequential forward search (SFS) method is generally used, where the next endmember is determined with a certain criterion based on the currently extracted endmember set. This letter proposes a new criterion which is related to the estimated endmember abundances. Compared to other sequential endmember extraction algorithms, the proposed method can find all the different endmembers faster. This letter also proposes to use the sequential forward floating search method as the substitute of SFS, which can improve the performance of all the sequential endmember extraction algorithms.

A Low-Computational-Complexity Algorithm for Hyperspectral Endmember Extraction: Modified Vertex Component Analysis

Endmember extraction represents one of the most challenging aspects of hyperspectral image processing. In this letter, a new algorithm for endmember extraction, named modified vertex component analysis (MVCA), is presented. This new technique outperforms the popular vertex component analysis (VCA) by applying a low-complexity orthogonalization method and by utilizing integer instead of floating-point arithmetic when dealing with hyperspectral data. The feasibility of this technique is demonstrated by comparing its performance with VCA on synthetic mixtures as well as on the well-known Cuprite hyperspectral image. MVCA shows promising results in terms of much lower computational complexity, still reproducing similar endmember accuracy than its original counterpart. Moreover, the features of this algorithm combined with state-of-the-art hardware implementations qualify MVCA as a good potential candidate for all those applications in which real time is a must.