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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >Hyperspectral Unmixing Based on Dual-Depth Sparse Probabilistic Latent Semantic Analysis
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Hyperspectral Unmixing Based on Dual-Depth Sparse Probabilistic Latent Semantic Analysis

机译：基于双深度稀疏概率潜在语义分析的高光谱分解

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摘要

This paper presents a novel approach for spectral unmixing of remotely sensed hyperspectral data. It exploits probabilistic latent topics in order to take advantage of the semantics pervading the latent topic space when identifying spectral signatures and estimating fractional abundances from hyperspectral images. Despite the contrasted potential of topic models to uncover image semantics, they have been merely used in hyperspectral unmixing as a straightforward data decomposition process. This limits their actual capabilities to provide semantic representations of the spectral data. The proposed model, called dual-depth sparse probabilistic latent semantic analysis (DEpLSA), makes use of two different levels of topics to exploit the semantic patterns extracted from the initial spectral space in order to relieve the ill-posed nature of the unmixing problem. In other words, DEpLSA defines a first level of deep topics to capture the semantic representations of the spectra, and a second level of restricted topics to estimate endmembers and abundances over this semantic space. An experimental comparison in conducted using the two standard topic models and the seven state-of-the-art unmixing methods available in the literature. Our experiments, conducted using four different hyperspectral images, reveal that the proposed approach is able to provide competitive advantages over available unmixing approaches.
机译：本文提出了一种新方法，用于遥感高光谱数据的光谱分解。它利用概率性潜在主题，以便在识别光谱特征并从高光谱图像估计分数丰度时利用潜在主题空间中的语义。尽管主题模型具有揭示图像语义的潜力，但它们仅在高光谱分解中用作直接的数据分解过程。这限制了它们提供光谱数据语义表示的实际能力。所提出的模型称为双深度稀疏概率潜在语义分析（DEpLSA），它利用两个不同级别的主题来利用从初始频谱空间中提取的语义模式，以缓解解混问题的不适定性质。换句话说，DEpLSA定义了第一级深层主题以捕获频谱的语义表示，并定义了第二级受限主题以估计该语义空间上的末端成员和数量。使用两种标准主题模型和文献中提供的七种最新技术进行的实验比较。我们的实验使用四个不同的高光谱图像进行，结果表明，与可用的分解方法相比，该方法能够提供竞争优势。

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