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

机译：光谱分解和亚像素分类：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：传统分类器通常忽略高光谱图像中的子像素细节。然而，一些最近的方法使用该信息来从具有粗略空间分辨率的图像生成高分辨率的土地覆盖图。在这方面的两个主要方面是：1）在每个像素处估计参考签名的分数丰度（频谱非混合）; 2）预测子像素尺度的类别分布（子像素分类）。这项研究提出了一些频谱解混以及子像素映射技术，这些技术考虑了通常被常规方法忽略的某些约束。在频谱分解方法的背景下，与ELM，STM和SVM相比，我们的主要贡献是对自动编码器的分析。在亚像素映射方法的情况下，我们的研究可以总结为建模深层自动编码器，以预测目标尺度下的空间分布。此外，我们还比较了自动编码器及其卷积对等体在学习粗糙图像特征方面的有效性。在提议的解混方法中，与SVM和STM相比，自动编码器方法具有更好的结果。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效果。还分析了所有这些技术对各种可调参数的敏感性。

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