ABSTRACT

Hyperspectral images (HSIs) are usually composed of hundreds of bands, which are highly correlated and redundant, leading to dimension disaster and high complexity of classification. In this paper, we propose an end-to-end dense spatial–spectral attention network (DSSAN) for hyperspectral image band selection to reduce the complexity of classification while ensuring the accuracy. In this network, an embeddable spatial–spectral attention module is designed, which can adaptively select the spectral bands from the raw input data. Moreover, this module is a plug-and-play complementary component and embedded in a dense convolutional network (DenseNet) for end-to-end training. The experimental results on two classic hyperspectral data sets demonstrate that the proposed method is superior to several mainstream band selection methods in classification accuracy and the selected band subset has lower redundancy, which can meet the application requirements.

摘要

高光谱图像（HSI）通常由数百个波段组成，这些波段高度相关且具有冗余性，从而导致尺寸灾难和分类的高度复杂性。在本文中，我们提出了一种用于高光谱图像波段选择的端到端密集空间光谱关注网络（DSSAN），以减少分类的复杂性，同时确保准确性。在该网络中，设计了一个可嵌入的空间光谱关注模块，该模块可以从原始输入数据中自适应地选择光谱带。此外，该模块是一个即插即用的互补组件，并嵌入到密集的卷积网络（DenseNet）中以进行端到端训练。