This article proposes a spectral–spatial method for classification of hyperspectral images (HSIs) by modifying traditional Auto-Encoder based on Majorization Minimization (MM) technique. The proposed method consists of suggesting three main modifications. First, to construct weights of Auto-Encoder, similarity angle map(SAM) criterion is used as regularization term. It is useful to extract spectral similarity of initial features. Second, to enhance the classification accuracy, fuzzy mode is used to estimate parameters. These modifications lead to create an extended Auto-Encoder based on MM (EAEMM). Third, to improve the performance of Auto-Encoder, multi-scale features (MSF) are extracted. In comparison with some of the state-of-the-art methods, the experimental results obtained using the proposed method (MSF-EAEMM) show that it significantly improves the classification accuracy of HSI classification.

本文提出了一种基于空间最小化（MM）技术的传统自动编码器修改方法，用于对高光谱图像（HSI）进行分类的光谱空间方法。所提出的方法包括建议三个主要修改。首先，为了构造自动编码器的权重，将相似角度图（SAM）准则用作正则化项。提取初始特征的光谱相似性非常有用。其次，为了提高分类精度，使用模糊模式来估计参数。这些修改导致创建基于MM（EAEMM）的扩展自动编码器。第三，为了提高自动编码器的性能，提取了多尺度特征（MSF）。与某些最新方法相比，