Adaptive Spectral-Spatial Multiscale Contextual Feature Extraction for Hyperspectral Image Classification

In this article, we propose an end-to-end adaptive spectral-spatial multiscale network to extract multiscale contextual information for hyperspectral image (HSI) classification, which contains spectral feature extraction (FE) and spatial FE subnetworks. In spectral FE aspect, different from previous methods where features are obtained in a single scale, which limits the accuracy improvement, we propose two schemes based on band grouping strategy, and the long short-time memory (LSTM) model is used for perceiving spectral multiscale information. In spatial subnetwork, on the foundation of existing multiscale architecture, the spatial contextual features which are usually ignored by previous literature are successfully obtained under the aid of convolutional LSTM (ConvLSTM) model. Besides, a new spatial grouping strategy is proposed for convenience of ConvLSTM to extract the more discriminative features. Then, a novel adaptive feature combining way is proposed considering the different importance of spectral and spatial parts. Experiments on three public data sets in HSI community demonstrate that our methods achieve competitive results compared with other state-of-the-art methods.

Automated summary

This article presents an end-to-end adaptive spectral-spatial multiscale network for extracting multiscale contextual information in hyperspectral images (HSI) classification, utilizing innovative methods and models for spectral and spatial feature extraction. Experimental results demonstrate competitive performance in comparison to other state-of-the-art methods in the field of HSI classification.