This study presents a deep extraction of localized spectral features and multi-scale spatial features convolution (LSMSC) framework for spectral-spatial fusion based classification of hyperspectral images (HSIs). First, adjacent spectral bands are grouped based on their similarity measurements, where the whole hypercube is partitioned into several sub-cubes, each corresponding to one band group. Then, the proposed localized spectral features extraction (LSF) strategy is used to extract localized spectral features, which are extracted from each band group using the 1D convolutional neural network (CNN). Meanwhile, the proposed HiASPP strategy is employed to extract the multi-scale features from the first several principal components of each sub-cube. Finally, the extracted spectral and spatial features are concatenated for spectral-spatial fusion based classification of HSI. Experiments conducted on three publicly available datasets have demonstrated that the proposed architecture outperforms several state-of-the-art approaches.

这项研究提出了深度提取局部光谱特征和多尺度空间特征卷积（LSMSC）框架，用于基于光谱空间融合的高光谱图像（HSI）分类。首先，基于相邻光谱带的相似性测量将其分组，其中整个超立方体被划分为几个子立方体，每个子立方体对应一个波段组。然后，提出的局部频谱特征提取（LSF）策略用于提取局部频谱特征，这些特征是使用1D卷积神经网络（CNN）从每个频带组中提取的。同时，提出的HiASPP策略用于从每个子多维数据集的前几个主要成分中提取多尺度特征。最后，将提取的光谱和空间特征串联起来，用于基于光谱空间融合的HSI分类。在三个可公开获得的数据集上进行的实验表明，所提出的体系结构优于几种最新方法。