Abstract

Recently, deep learning (DL)-based methods have attracted increasing attention for hyperspectral images (HSIs) classification. However, the complex structure and limited number of labelled training samples of HSIs negatively affect the performance of DL models. In this paper, a spectral-spatial classification method is proposed based on the combination of local and global spatial information, including extended multi-attribute profiles and multiscale Gabor features, with sparse stacked autoencoder (GEAE). GEAE stacks the spatial and spectral information to form the fused features. Also, GEAE generates virtual samples using weighted average of available samples for expanding the training set so that many parameters of DL network can be learned optimally in limited labelled samples situations. Therefore, the similarity between samples is determined with distance metric learning to overcome the problems of Euclidean distance-based similarity metrics. The experimental results on three HSIs datasets demonstrate the effectiveness of the GEAE in comparison to some existing classification methods.

摘要

最近，基于深度学习 (DL) 的方法在高光谱图像 (HSI) 分类方面引起了越来越多的关注。然而，HSI 的复杂结构和有限数量的标记训练样本会对 DL 模型的性能产生负面影响。在本文中，提出了一种基于局部和全局空间信息组合的光谱空间分类方法，包括扩展的多属性轮廓和多尺度Gabor特征，具有稀疏堆叠自动编码器（GEAE）。GEAE 堆叠空间和光谱信息以形成融合特征。此外，GEAE 使用可用样本的加权平均值生成虚拟样本以扩展训练集，以便在有限的标记样本情况下优化学习 DL 网络的许多参数。所以，通过距离度量学习确定样本之间的相似性，以克服基于欧几里德距离的相似性度量的问题。三个 HSI 数据集的实验结果证明了 GEAE 与一些现有分类方法相比的有效性。