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ABSTRACT: Endmember extraction (EE) has been widely utilized to extract spectrally unique and singular spectral signatures for spectral mixture analysis of hyperspectral images. Recently, spatial-spectral EE (SSEE) algorithms have been proposed to achieve superior performance over spectral EE (SEE) algorithms by taking both spectral similarity and spatial context into account. However, these algorithms tend to neglect anomalous endmembers that are also of interest. Therefore, in this paper, an improved SSEE (iSSEE) algorithm is proposed to address such limitation of conventional SSEE algorithms by accounting for both anomalous and normal endmembers. By developing simplex projection and simplex complementary projection, all the hyperspectral pixels are projected into a simplex determined by the normal endmembers extracted in conventional SSEE algorithms. As a result, anomalous endmembers are identified iteratively by utilizing the l <sub>2</sub><sup>∞</sup> norm to find the maximum simplex complementary projection. In order to determine how many anomalous endmembers are to be extracted, a novel Residual-be-Noise Probability-based algorithm is also proposed by elegantly utilizing the spatial-purity map generated in the previous SSEE step. Experimental results on both synthetic and real datasets demonstrate that simplex projection errors can be significantly reduced by identifying both anomalous and normal endmembers in the proposed iSSEE algorithm. It is also confirmed that the performance of the proposed iSSEE algorithm clearly outperforms that of SEE algorithms since both spatial context and spectral similarity are utilized.
IEEE Transactions on Geoscience and Remote Sensing 12/2011; · 2.89 Impact Factor
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IEEE T. Geoscience and Remote Sensing. 01/2011; 49:4210-4222.
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ABSTRACT: Spectral mixture analysis (SMA) has been widely utilized to address the mixed-pixel problem in the quantitative analysis of hyperspectral remote sensing images, in which endmember extraction (EE) plays an extremely important role. In this paper, a novel algorithm is proposed to integrate both spectral similarity and spatial context for EE. The spatial context is exploited from two aspects. At first, initial endmember candidates are identified by determining the spatial purity (SP) of pixels in their spatial neighborhoods (SNs). Several SP measurements are investigated at both intensity level and feature level. In order to alleviate local spectra variability, the average of the pixels in pure SNs are voted as endmember candidates. Then, the spatial connectivity is utilized to merge spatially related endmember candidates by finding connection paths in a graph so that the number of endmember candidates is further reduced, which results in computational efficiency and better performance in SMA by alleviating global spectral variability. Experimental results on both synthetic and real hyperspectral images demonstrate that the proposed SP based EE (SPEE) algorithm outperforms the other popular EE algorithms. It is also observed that feature-level SP measurements are more distinguishable than intensity-level SP measurements to discriminate pure SNs from mixed SNs.
IEEE Transactions on Geoscience and Remote Sensing 10/2010; · 2.89 Impact Factor
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ABSTRACT: Due to the spatial-resolution limitation, mixed pixels containing energy reflected from more than one type of ground objects are widely present in remote sensing images, which often results in inefficient quantitative analysis. To effectively decompose such mixtures, a fully constrained linear unmixing algorithm based on a multichannel Hopfield neural network (MHNN) is proposed in this letter. The proposed MHNN algorithm is actually a Hopfield-based architecture which handles all the pixels in an image synchronously, instead of considering a per-pixel procedure. Due to the synchronous unmixing property of MHNN, a noise energy percentage (NEP) stopping criterion which utilizes the signal-to-noise ratio is proposed to obtain optimal results for different applications automatically. Experimental results demonstrate that the proposed multichannel structure makes the Hopfield-based mixture analysis feasible for real-world applications with acceptable time cost. It has also been observed that the proposed MHNN-based mixture-analysis algorithm outperforms the other two popular linear mixture-analysis algorithms and that the NEP stopping criterion can approach optimal unmixing results adaptively and accurately.
IEEE Geoscience and Remote Sensing Letters 08/2010; · 1.56 Impact Factor
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IEEE T. Geoscience and Remote Sensing. 01/2010; 48:3434-3445.
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ABSTRACT: Spectral Mixture Analysis (SMA) has been widely utilized for hyperspectral remote sensing image analysis and quantification to address the mixed pixel problem, in which Endmember Extraction (EE) plays an extremely important role. Distinct from the traditional EE algorithms which are only based on spectral information, a novel EE algorithm integrating spectral characteristics and spatial distribution is proposed in this paper. Purity of pixels presenting in a spatial neighborhood (SN) is examined by the Singular Value Decomposition (SVD) based on not only spectral characteristic but also spatial distribution, which effectively addresses the spectral deviation problem. Spectral deviation inside an SN is eliminated by selecting the average of the pixels in pure SNs as endmember candidates, while spectral deviation among different areas in an image is eliminated by clustering these endmember candidates. In addition, a graph theory based spatial refinement algorithm is proposed to reduce the number of endmember candidates, which can save a lot computation in the subsequent clustering step. Experimental results on AVIRIS hyperspectral data demonstrate that the proposed Spectral-spatial EE algorithm outperforms the other three popular EE algorithms, N-finder algorithm (N-FINDR), unsupervised fully constrained least squares (UFCLS) algorithm, and the automated morphological endmember extraction (AMEE) algorithm.
Industrial Electronics and Applications, 2009. ICIEA 2009. 4th IEEE Conference on; 06/2009
