Hyperspectral Endmember Extraction by (μ + λ) Multiobjective Differential Evolution Algorithm Based on Ranking Multiple Mutations

A method of incorporating the multi-mixture pixel model into hyperspectral endmember extraction is presented and discussed. A vast majority of hyperspectral endmember extraction methods rely on the linear mixture model to describe pixel spectra resulting from mixtures of endmembers. Methods exist to unmix hyperspectral pixels using nonlinear models, but rely on severely limiting assumptions or estimations of the nonlinearity. This paper will present a hyperspectral pixel endmember extraction method that utilizes the bidirectional reflectance distribution function to model microscopic mixtures. Using this model, along with the linear mixture model to incorporate macroscopic mixtures, this method is able to accurately unmix hyperspectral images composed of both macroscopic and microscopic mixtures. The mixtures are estimated directly from the hyperspectral data without the need for a priori knowledge of the mixture types. Results are presented using synthetic datasets, of multi-mixture pixels, to demonstrate the increased accuracy in unmixing using this new physics-based method over linear methods. In addition, results are presented using a well-known laboratory dataset.

The spatial resolution of hyperspectral data is low, so there are a large number of mixed pixels, which is also one of the main reasons that reduce the accuracy of hyperspectral image target classification. Hyperspectral unmixing is an important subject in the field of remote sensing. Hyperspectral unmixing generally consists of three steps: reduction, endmember extraction and inversion. As one of the key steps of hyperspectral unmixing, efficient and rapid endmember extraction is an important object in hyperspectral remote sensing. In this paper, the endmember extraction of hyperspectral data is implemented based on PCA and a new SGA algorithm, which solves the dimension limitation of traditional SGA algorithm and the new SGA algorithm without data redundancy caused by data dimensionality reduction.

Hyperspectral image unmixing is an important part of hyperspectral data analysis. The mixed pixel decomposition consists of two steps, endmember (the unique signatures of pure ground components) extraction and abundance (the proportion of each endmember in each pixel) estimation. Recently, a Discrete Particle Swarm Optimization algorithm (DPSO) was proposed for accurately extract endmembers with high optimal performance. However, the DPSO algorithm shows very high computational complexity, which makes the endmember extraction procedure very time consuming for hyperspectral image unmixing. Thus, in this paper, the DPSO endmember extraction algorithm was parallelized, implemented on the CUDA (GPU K20) platform, and evaluated by real hyperspectral remote sensing data. The experimental results show that with increasing the number of particles the parallelized version obtained much higher computing efficiency while maintain the same endmember exaction accuracy.

Endmember extraction is a key step in hyperspectral unmixing. This paper proposes a new endmember extraction framework based on the swarm intelligence algorithm. We adopt a discrete structure because pixels exist within a discrete frame. Traditional swarm intelligence algorithms produce stacked solutions based on similar endmembers in the same class. We introduce a “distance” factor into the objective function to limit the number of endmembers per class. We then propose three endmember extraction methods based on the artificial bee colony (ABC), ant colony optimization (ACO) and particle swarm optimization (PSO) algorithms. Experiments with both simulated and actual hyperspectral image data indicate that the proposed framework can significantly improve the accuracy of endmember extraction.

The endmember extraction is a challenging problem in spectral unmixing (SU) of a mixed pixel in hyperspectral imagery. There are plenty of attempts to solve the endmember extraction problem. Still, the pure pixel assumption-based algorithms have probably been used most in solving the endmember extraction of SU due to the light computational burden. These pure pixel assumption-based algorithms usually follow one of the criteria: (1) Maximum simplex volume or (2) Extreme projection on a subspace. We propose a novel integrated framework that uses both the criteria mentioned above and the proposed one is referred to as the Convexity-based Pure Index (CPI) algorithm. The CPI generates a fixed number of convex sets based on the number of available bands in the hyperspectral image. The algorithm defines the purity score based on the availability of pixels in the convex sets for the two-band data. The CPI has been compared with contemporary algorithms such as Automatic Target Generation Process (ATGP), Vertex Component Analysis (VCA), Pixel Purity Index (PPI), Successive Volume MAXimization (SVMAX), Alternating Volume MAXimization (AVMAX), TRIple-P: P-norm based Pure Pixel identification (TRIP), Successive Decoupled Volume Max–Min (SDVMM), Negative ABundance-Oriented (NABO), and Entropy-based Convex Set Optimization (ECSO). The metrics, Spectral Angle Distance (SAD) and Spectral Information Divergence (SID) used in the comparison were improved up to 5.9% and 9%, respectively. The CPI outperforms prevailing algorithms on real benchmark data and new AVIRIS-NG data. The robustness of the CPI is also tested for various noisy synthetic data. The efficacy of the proposed algorithm is also tested by using qualitative analysis by visualizing the spectra comparison, and abundance maps for all real data.

PDF HTML阅读 XML下载 导出引用 引用提醒 基于Hyperion的锡林郭勒草原光合植被、非光合植被覆盖度估算 DOI: 10.5846/stxb201308142075 作者: 作者单位: 中国科学院遥感与数字地球研究所,中国科学院遥感与数字地球研究所,中国科学院遥感与数字地球研究所 作者简介: 通讯作者: 中图分类号: 基金项目: 国家科技支撑计划(2011BAH23B04);国家科技重大专项(21-Y30B05-9001-13/15) Estimating fractional cover of photosynthetic vegetation and non-photosynthetic vegetation in the Xilingol steppe region with EO-1 hyperion data Author: Affiliation: Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences,Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences,Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:掌握草原生态系统光合植被覆盖度(fPV)与非光合植被覆盖度(fNPV)时空动态对了解干旱半干旱草原生态系统特征(覆盖状况、火灾负载、载畜量、干扰及恢复等)及进行科学、有效地草地资源管理具有重要的意义。选取锡林郭勒典型草原为试验区,以Hypeiron高光谱数据为数据源,利用NDVI-CAI三元线性混合模型对试验区fPV和fNPV的时空动态分布进行了估算,并对不同端元选择方法(最小包含端元特征法、纯净象元指数法和实测法)对估算结果的影响进行了比较分析。研究结果表明,NDVI-CAI三元线性混合模型是同时估测锡林郭勒草原fPV和fNPV的有效方法,且估算的fPV和fNPV的季节变化与牧草的物候发育特征相吻合。不同端元选择方法对估算精度具有一定的影响,其中基于最小包含端元特征法提取端元进行估算的精度最高,fPV估算的均方根误差RMSE=4.57,估算精度EA=91.2%;fNPV估算的RMSE=5.90,EA=67.91%(样本数N=52)。 Abstract:Quantitative estimation of the spatial and temporal dynamics of the fractional cover of photosynthetic vegetation (fPV) and non-photosynthetic vegetation (fNPV) in semi-arid grasslands is critical for understanding grassland conditions such as vegetation abundance, drought severity, fire fuel load, stocking rate, and disturbance events and recovery. It is also important for scientific grassland resource management. Over the past several decades, remote sensing has become an important tool for estimating the fractional cover of vegetation, which is a key descriptor of ecosystem function. However, most efforts have been devoted to the estimation of fPV rather than fNPV, although the latter is equally important, especially in arid and semi-arid ecosystems. This study describes a linear unmixing approach for estimating fPV and fNPV in the Xilingol steppe region with hyperspectral and field investigation data. Five Hyperion images acquired on April 4, May 20, July 27, August 30, and November 15 in 2012 and a field-measured spectral library were utilized to explore the spectral feature space of fPV and fNPV in order to validate the feasibility of a linear unmixing model. This model is based on two complementary spectral indices of vegetation that have been used in remote sensing analyses to discriminate green and dry vegetation from soils: the Normalized Difference Vegetation Index (NDVI) and the Cellulose Absorption Index (CAI). Different end-member extraction methods, including the Minimum-Volume Enclosing (MVE) method, the Pixel Purity Index (PPI) method, and a field measurement method, were adopted to retrieve the end-member values of photosynthetic vegetation, non-photosynthetic vegetation, and bare soil, respectively, from NDVI and CAI. Then, the influence of end-member extraction on the accuracy of the fPV and fNPV estimation was evaluated through comparison with field-measured fPV and fNPV values acquired from classifications performed on fisheye photos (N=52). Subsequently, the optimum unmixing strategy was utilized to retrieve the temporal dynamics of fPV and fNPV in a fenced area, where the grassland was not influenced by human activities, so that the usefulness of these fractional coverage indices could be validated by checking their consistency with the phenology of natural grassland. The result shows that the linear unmixing model based on NDVI and CAI was effective for estimating fPV and fNPV in the Xilingol steppe region. The NDVI-CAI feature space follows a triangular distribution, where the three vertexes represent photosynthetic vegetation, non-photosynthetic vegetation, and bare soil, meeting the essential requirements of the linear unmixing model. The estimation accuracy was different for the different end-member extraction methods. The MVE-based estimation had the highest accuracy, with estimated accuracy of 91.2% and 67.91% for fPV and fNPV, respectively, followed by the PPI-based estimation (91.0% and 59.5% for fPV and fNPV, respectively) and the field-measurement-based estimation (86.2% and 56.7% for fPV and fNPV, respectively). In general, the estimation accuracy was higher for fPV than fNPV, and the field-measured end-member performed worse than the image end-member, which was probably due to the inconsistency between the field-measured spec and the Hyperion spec. Additionally, the temporal dynamics of fPV and fNPV were confirmed to be consistent with the phenological seasonal change in natural grasslands. Therefore, the method proposed here can be used to monitor the temporal and spatial variations of fPV and fNPV in semi-arid grasslands. 参考文献 相似文献 引证文献

Endmember extraction (EE) is one of the most important issues in hyperspectral mixture analysis, and it is also one of the most challenging tasks due to the intrinsic complexity of remote sensing images and the lack of priori knowledge. In recent years, a number of EE methods have been developed, and several different optimization objectives have been proposed from different perspectives. In all of these methods, there is only one objective function to be optimized, which represents a specific characteristic of the remote sensing image. However, one single-objective function may not provide satisfactory results because of the complexity of remote sensing images. In this paper, a multiobjective discrete particle swarm optimization algorithm (MODPSO) is utilized to tackle the problem of EE, where two objective functions, namely, volume maximization and root-mean-square error (RMSE) minimization are simultaneously optimized. The result set of Pareto-optimal solutions contains a number of non-dominated solutions, from which the user can judge relatively and pick up the most promising one according to the problem requirements. Experiments on two real hyperspectral images were conducted to evaluate the proposed MODPSO algorithm, which confirmed the effectiveness of the proposed algorithm.

Environmental/economic power dispatch problem using multi-objective differential evolution algorithm

Hyperspectral unmixing aims to estimate endmember spectra and their corresponding abundance fractions at the subpixel scale, which is a critical preprocessing step for quantitative analysis of hyperspectral remote sensing imagery. While deep learning-based methods have achieved remarkable progress, three fundamental challenges remain: (i) reliance on a single shared spatial prior that cannot decouple the heterogeneous spatial patterns of different land covers; (ii) the lack of synergy in jointly optimizing endmember extraction and abundance estimation; (iii) the poor robustness of unsupervised training to complex mixtures, noise, and class imbalance. To address these issues, we propose a novel unsupervised unmixing framework that integrates adaptive orthogonal multi-faceted graph representation with curriculum learning. Specifically, we design an Adaptive Orthogonal Multi-Faceted Graph Generator (AOMFG) to learn a set of independent orthogonal graph structures, achieving spatially informed decoupling of land cover patterns. Then, a dual-branch collaborative optimization network is constructed: a Graph Convolutional Network (GCN) branch that incorporates the learned spatial topological priors for abundance estimation, and a 1D Convolutional Neural Network (1DCNN) branch that employs a query-attention mechanism to adaptively aggregate pure spectral features for endmember extraction. Finally, we introduce a three-stage curriculum learning strategy that progressively fine-tunes the model, which significantly enhances its performance. Extensive experiments on three widely used real-world benchmark datasets demonstrate that our proposed framework consistently outperforms state-of-the-art methods in both endmember extraction and abundance estimation accuracy. Comprehensive ablation studies, parameter sensitivity analysis, and noise robustness tests further validate the effectiveness of each core component.

Abstract. An essential step in the characterization of surface materials using hyperspectral image analysis is image classification using endmembers. Spectral unmixing is the best method for hyperspectral image classification. This method assumes that the pixel-topixel variability in scene results from varying proportions of spectral endmembers. Spectral endmembers can be derived from the imagery or measurements in the laboratory or field. The primary objective of this paper was to assess the ability of extracted endmembers against some different solutions for extraction of endmembers for hyperspectral image classification. In this paper, we compared the Pixel Purity Index (PPI) and the Sequential Maximum Angle Convex Cone (SMACC) as two popular methods of endmember extraction with library and field spectral. We used spectral information divergence for detection desirable endmembers from field spectral. For accuracy assessment of spectral mixture analysis and production of endmember abundance images for each of methods, the linear spectral unmixing algorithm is used. After a comparison between the results of these methods, it has been verified that field spectral have a better classification result in comparing with other endmember extraction methods. Also, the PPI has reliable results as an automatic endmember extraction method in comparing.

Endmember extraction (EE) is defined as the process of identifying a set of pure and unique spectral signatures of the materials combined at pixel level in a remotely sensed hyperspectral scene. These extracted signatures are utilized to decompose scene into abundance fractions by means of Spectral Mixture Analysis (SMA) model Most of algorithms used for EE, are established on spectral discrimination without any attention to spatial context and distribution of image pixels. Some years ago several algorithms are implied which applies spatial context in addition to spectral discrimination in a distinct module such as SPP and SSPP. In this paper, we propose a novel Cluster-based Spatial Border Removal Preprocessor (CSBRP) by removing spatial borders of cluster map and calculate spectral purity weight of residual pixels in order to look for spatially homogenous and spectrally pure pixels with help of spectral purity weight threshold in a distinct module. The purpose of CSBRP is to improve RMSE reconstruction of original image and a new metric defined as Efficiency by reducing the processing time needed for EE. Experimental results on the proposed preprocessor outperform the state of the art EE methods, SPP and SSPP in real Aviris hyperspectral Salinas and Indian Pines images.

Hyperspectral unmixing is a critical task in remote sensing, challenged by complex mixing mechanisms and long-range spatial dependency modeling. To address the limitations of existing methods in capturing spatial structures and frequency representations, we propose SFMamba, a novel spatial-frequency collaborative unmixing framework. The framework includes two key modules: (1) a multi-view spatial mamba module based on state space model, which captures directional spatial dependencies to enhance contextual representation; and (2) a hyperspectral fourier mixer, which integrates fourier transform-based frequency features to strengthen the spectral modeling of endmembers. Experiments on the Urban and Jasper Ridge datasets demonstrate that the proposed method achieves superior performance in endmember extraction, with competitive results in abundance estimation, thus showing strong potential in hyperspectral unmixing tasks. This work provides a novel and effective solution for hyperspectral unmixing with spatial-frequency collaboration.

Deep learning-based hyperspectral unmixing (HU) is getting increasing attention in the field of remote sensing, aiming at endmember extraction and abundance estimation at pixel scale. However, many existing deep learning-based unmixing methods base on linear mixing models, neglecting complex nonlinear light scattering interactions. Furthermore, these methods often treat all spectral bands indiscriminately, ignoring characteristic differences between endmembers, hampering endmember separation. To address these issues, we present BU-Net, a novel approach for HU based on the generalized bilinear mixing model (GBM), which is a two-stream stacked autoencoder architecture designed to enhance inter-class separability. In the encoder, we employ 3D convolutions with multiple receptive field to extract multiscale spatial and spectral features simultaneously. Additionally, we design a novel band selection based on inter-class separability (BSICS), which identifies bands with inter-class separability (BICS) and the obtained bands are taken as an additional stream for improving performance. In the decoder, BU-Net develops a two-stream structure encompassing linear and bilinear elements, aligning with the theoretical components and constraints of GBM. To further enhance separability between endmembers during training, we use the spectral angle distance between BICS and its reconstruction as a loss regularization term. Moreover, we utilize materials' representative pixels obtained in the process of BSICS to initialize endmembers, which offers effective guidance for modeling the spectral properties. Experimental results on synthetic and real hyperspectral datasets show that our method outperforms state-of-the-art methods. This novel approach addresses limitations of linear mixing models while leveraging deep learning to improve accuracy of HU.

Hyperspectral unmixing (HU) has become an important technique in exploiting hyperspectral data since it decomposes a mixed pixel into a collection of endmembers weighted by fractional abundances. The endmembers of a hyperspectral image (HSI) are more likely to be generated by independent sources and be mixed in a macroscopic degree before arriving at the sensor element of the imaging spectrometer as mixed spectra. Over the past few decades, many attempts have focused on imposing auxiliary regularizes on the conventional nonnegative matrix factorization (NMF) framework in order to effectively unmix these mixed spectra. As a promising step toward finding an optimum regularizer to extract endmembers, this paper presents a novel blind HU algorithm, referred to as Kurtosis-based Smooth Nonnegative Matrix Factorization (KbSNMF) which incorporates a novel regularizer based on the statistical independence of the probability density functions of endmember spectra. Imposing this regularizer on the conventional NMF framework promotes the extraction of independent endmembers while further enhancing the parts-based representation of data. Experiments conducted on diverse synthetic HSI datasets (with numerous numbers of endmembers, spectral bands, pixels, and noise levels) and three standard real HSI datasets demonstrate the validity of the proposed KbSNMF algorithm compared to several stateof- the-art NMF-based HU baselines. The proposed algorithm exhibits superior performance especially in terms of extracting endmember spectra from hyperspectral data; therefore, it could uplift the performance of recent deep learning HU methods which utilize the endmember spectra as supervisory input data for abundance extraction.

With the advancement in technology, hyperspectral images have potential applications in the field of remote sensing due to their high spectral resolution. Despite the hyperspectral image providing abundant information, its analysis suffers from the problem of high dimensionality. Hence, Dimensionality Reduction (DR) is an essential task in all hyperspectral image analysis. Band Selection, which is one of the DR techniques, is still a challenging issue even though many algorithms have been developed. To provide remedy for this issue, this paper explores a novel approach for band selection using K-means clustering on statistical feature in hyperspectral images. The proposed method of clustering based band selection for DR is simple and accurate. A reliable estimate of number of bands to be selected is provided by Virtual Dimensionality (VD). Informative bands preserving maximum information are selected based on the statistical feature, the variance using K-means Clustering technique. Further, our proposed work involves the utilization of the effectiveness of Simplex Growing Algorithm (SGA) on endmember extraction in association with clustering based band selection. Using Fully Constrained Least Squares (FCLS) method, abundance fraction is estimated based on endmember signatures, which are derived using Endmember Extraction Algorithm (EEA). The proposed work is investigated and compared with that of N-FINDR and Vertex Component Analysis (VCA) algorithms. The performance of the proposed algorithm is evaluated using Root Mean Square Error (RMSE), Spectral Angle Distance (SAD) and computation time. Experimental results show that the proposed clustering based band selection with SGA endmember extraction algorithm reduces the average SAD by 8 to 10 % and the average RMSE by nearly 1 %, compared to that of N-FINDR and VCA algorithms. In terms of computation time, the proposed band selection based DR with SGA algorithm is seven times faster than conventional transform based DR with SGA algorithm.