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Spectral Unmixing And Sub-pixel Classification: Analysis of learning strategies

机译：光谱解密和子像素分类：学习策略分析

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Sub-pixel details in the hyperspectral images are generally ignored by the conventional classifiers. However, some recent approaches use this information to generate fine resolution land cover maps from images having coarse spatial resolution. Two main aspects in this regard are: 1) estimation of fractional abundances of the reference signatures at each pixel (spectral un-mixing); and 2) prediction of class distributions at sub-pixel scale (sub-pixel classification). This study proposes some spectral unmixing as well as sub-pixel mapping techniques that take in to account certain constraints which are usually ignored by the conventional approaches. In the context of spectral unmixing methods, our main contribution is the analysis of auto-encoders when compared with ELM, STM and SVM. In case of sub-pixel mapping methods, our study may be summarized as the modelling deep auto-encoders for predicting the spatial distributions at target scale. Also, we have compared the effectiveness of Auto-Encoders and their convolutional counterparts in learning the coarse image features. Among the proposed unmixing approaches, autoencoder approach has given better results when compared to that of SVM and STM. The deep learning based sub-pixel mapping approaches have also produced good results, even for complex scenes. The sensitivities of all these techniques towards various tunable parameters are also analyzed.
机译：通常由传统分类器忽略高光谱图像中的子像素细节。然而，最近的一些方法使用该信息从具有粗糙空间分辨率的图像产生微分辨率的陆地覆盖映射。这方面的两个主要方面是：1）估计每个像素（光谱杂散）处的参考签名的分数丰度; 2）子像素刻度下的类分布的预测（子像素分类）。本研究提出了一些光谱解波以及用于考虑通常被传统方法忽略的某些约束的子像素映射技术。在光谱解密方法的背景下，我们的主要贡献是与ELM，STM和SVM相比的自动编码器的分析。在子像素映射方法的情况下，我们的研究可以总结为模型深度自动编码器，用于预测目标规模以空间分布。此外，我们已经比较了自动编码器的有效性及其卷积对应于学习粗略图像特征。在提出的解密方法中，与SVM和STM相比，AutoEncoder方法已经获得了更好的结果。即使对于复杂的场景，基于深度学习的子像素映射方法也产生了良好的结果。还分析了所有这些技术朝各种可调参数的敏感性。

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