Automatically Adjustable Multi-Scale Feature Extraction Framework for Hyperspectral Image Classification

Recently, deep learning-based methods have shown the great potential in hyperspectral image (HSI) classification. Nevertheless, feature extraction by convolutional neural network (CNN) is often performed on only one scale, resulting in multi-scale information loss. To address this problem, in this paper, we propose an automatically adjustable multi-scale feature extraction framework (A²MFE-Framework) for hyperspectral classification, including a scale reference network and two scale transformation networks. With the well-designed architecture, A²MFE-Framework can not only extract multiscale features, but also automatically change the network structure to match input features of different scales. Experimental results on two benchmark HSI datasets demonstrate that the A²MFE-Framework can better capture multi-scale features of different objects via an automatically adjustable feature extraction framework with higher classification accuracy compared with previous methods.