- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Low-rank and sparse matrix decomposition with background position estimation for hyperspectral anomaly detection
摘要： Hyperspectral anomaly detection (AD) has attracted much attention over the last 20 years. It distinguishes pixels with significant spectral differences from the background without any prior knowledge. The low-rank and sparse matrix decomposition (LRaSMD)-based detector has been applied to AD, where the anomaly value is measured by Euclidean distance based on the sparse component. However, the background interference in sparse component seriously increases the false alarm rate and influences the detection of real anomalies. In this paper, a novel AD method based on LRaSMD and background position estimation is proposed, which aims to suppress background interference in the sparse component for a better separation between background and anomalies. Firstly, the original sparse matrix is obtained using the traditional LRaSMD method. Secondly, the abundance maps are constructed by the sequential maximum angel convex cone (SMACC) endmember extraction model. Thirdly, considering that the anomalies occupy only a few pixels with a low probability, the coordinate positions of background pixels are estimated through these abundance maps. Finally, the spectra corresponding to these positions in the original sparse matrix are replaced with zero vectors, and the final anomaly value is calculated based on the improved sparse matrix. The proposed method achieves an outstanding performance by considering both the spectral and spatial characteristics of anomalies. Experimental results on synthetic and real-world hyperspectral datasets demonstrate the superiority of the proposed method compared with several state-of-the-art AD detectors.
关键词： Anomaly detection,Background estimation,Low-rank and sparse matrix decomposition,Hyperspectral imagery,Endmember extraction
更新于2025-09-23 15:23:52
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[IEEE 2018 OCEANS - MTS/IEEE Kobe Techno-Ocean (OTO) - Kobe (2018.5.28-2018.5.31)] 2018 OCEANS - MTS/IEEE Kobe Techno-Oceans (OTO) - An Improved Method for Modeling the Island Photovoltaic Power Generation System With MPPT
摘要： Simplex maximum distance (SMD) is an algorithm based on that the pixel with the biggest distance from simplex formed by known endmembers is most likely to be the next endmember. However, SMD involves calculation of some intermediate variables, such as simplex’s normal vector, and intersection point of simplex and line, leading to computation complexity. In addition, high brightness points, outliers and isolated noise points in hyperspectral image are often extracted as endmembers in SMD. To overcome these two shortages, an improved simplex maximum distance (ISMD) algorithm is presented in the paper. To simplify computation procedure, ISMD defines the distance from pixel to simplex as ratio of volumes of parallel polyhedrons with adjacent dimensions. Once distances of all pixels from existing simplex are received, a set of similar pixels was selected from multiple pixels with a larger distance according to the spectral angle. The set of pixels is averaged to be the new endmember. The ISMD algorithm was assessed using simulated and real AVIRIS images. Compared with SMD, ISMD better extracted real endmembers in simulated image. And spectral angle between endmember obtained by ISMD and corresponding mineral from USGS spectral library is less for AVIRIS image.
关键词： endmember extraction,hyperplane,hyperspectral image,simplex maximum distance formatting
更新于2025-09-11 14:15:04
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Unsupervised Nonlinear Spectral Unmixing of Satellite Images Using the Modified Bilinear Model
摘要： Episodes of mixing pixels in satellite imageries are more prevalent. Hence, spectral unmixing approach is used to perform the sub-pixel classification of satellite images. Many unmixing works were done based on the assumption that the pixels are linearly mixed (single interaction) but in real scenarios, the pixels are nonlinearly mixed due to interactions. Fan model and generalized bilinear model consider only the bilinear interactions for nonlinear unmixing. In reality, multiple interactions between the various classes are also present in the image. In this work, a new model, ‘modified bilinear model’ is proposed to perform the nonlinear unmixing process that considers the entire single, bilinear and multiple interactions into account. This system adaptively changes the mixing model on per pixel basis depending on the nonlinearity parameter. It has been tested with the multispectral, synthetic and real hyperspectral datasets and also illustrated notable advantages compared with the other methods.
关键词： Multiple interaction,Spectral unmixing,Endmember extraction,Nonlinear unmixing
更新于2025-09-10 09:29:36
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A novel endmember extraction method using sparse component analysis for hyperspectral remote sensing imagery
摘要： The spectral unmixing (SU) technique is an effective method of solving the mixed pixel problem in the hyperspectral remote sensed imagery (HSI). During the process, endmember extraction algorithm (EEA) is significant for the creation of material abundance maps. However, the traditional EEAs are not very reliable due to the low resolution of sensor and the complex diversity of land cover feature distribution. In addition, the mutually independent endmember assumption will be affected accordingly. In order to overcome the above limitations, a novel endmember extraction method using sparse component analysis for hyperspectral remote sensing imagery (EESCA) has been presented in this paper. EESCA assumes that each pixel in the image scene is a sparse linear mixture of all endmembers. First, the hyperline clustering algorithm is incorporated to consider the subspace clustering of all pixels after the initialization of endmember mixing matrix. It enlarges differences among ground objects and helps finding endmembers with smaller spectrum divergences. After that, the K-SVD is proposed to search the real endmembers for sparse representations with coefficients summarized in the mixing matrix. The method transfers the pure endmember extraction problem into an optimization problem by minimizing the residual errors. Four state-of-the art methods are implemented to make comparisons with the performance of EESCA. The robustness of the proposed algorithm is verified through both simulated images and real satellite images. Experimental results show that the EESCA outperforms other methods in spectral angle distance (SAD) and root-mean-square-error (RMSE), and especially could identify accurate endmembers for ground objects with smaller spectrum divergences.
关键词： sparse component analysis,endmember extraction,Hyperspectral imagery,spectral unmixing
更新于2025-09-10 09:29:36