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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >Spectral and Spatial Complexity-Based Hyperspectral Unmixing
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Spectral and Spatial Complexity-Based Hyperspectral Unmixing

机译：基于光谱和空间复杂度的高光谱分解

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摘要

Hyperspectral unmixing, which decomposes pixel spectra into a collection of constituent spectra, is a preprocessing step for hyperspectral applications like target detection and classification. It can be considered as a blind source separation (BSS) problem. Independent component analysis, which is a widely used method for performing BSS, models a mixed pixel as a linear mixture of its constituent spectra weighted by the correspondent abundance fractions (sources). The sources are assumed to be independent and stationary. However, in many instances, this assumption is not valid. In this paper, a complexity-based BSS algorithm is introduced, which studies the complexity of sources instead of the independence. We extend the 1-D temporal complexity, which is called complexity pursuit that was proposed by Stone, to the 2-D spatial complexity, which is named spatial complexity BSS (SCBSS), to describe the spatial autocorrelation of each abundance fraction. Further, the temporal complexity of spectrum is combined into SCBSS to account for the spectral smoothness, which is termed spectral and spatial complexity BSS. More importantly, a strict theoretic interpretation is given, showing that the complexity-based BSS is very suitable for hyperspectral unmixing. Experimental results on synthetic and real hyperspectral data demonstrate the advantages of the proposed two algorithms with respect to other methods.
机译：高光谱解混将像素光谱分解为一组组成光谱，是用于高光谱应用（如目标检测和分类）的预处理步骤。可以将其视为盲源分离（BSS）问题。独立成分分析是一种广泛使用的执行BSS的方法，它会将混合像素建模为其组成光谱的线性混合，并由相应的丰度分数（来源）加权。假定光源是独立且固定的。但是，在许多情况下，此假设无效。本文介绍了一种基于复杂度的BSS算法，该算法研究源的复杂度而不是独立性。我们将一维时间复杂度（由斯通提出的称为复杂度追求）扩展到二维空间复杂度（称为空间复杂度BSS（SCBSS）），以描述每个丰度分数的空间自相关。此外，将频谱的时间复杂度组合到SCBSS中以说明频谱的平滑度，这称为频谱和空间复杂度BSS。更重要的是，给出了严格的理论解释，表明基于复杂度的BSS非常适合于高光谱分解。综合和真实高光谱数据的实验结果证明了所提出的两种算法相对于其他方法的优势。

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