Attention Symbiotic Neural Network for Hyperspectral Image Refined Classification Based on Relative Water Content Retrieval

Abstract

Hyperspectral image (HSI) classification appro- aches achieve significant improvements with the proposal and application of deep learning algorithms. However, due to the end-to-end structure of the deep learning model, the exploration of intrinsic physical-chemical properties in HSI data is insufficient, which restricts the extraction of diagnostic features and impedes the improvement of intraclass classification performance. Moreover, the synergetic spectral-spatial feature extraction in deep learning model is limited owing to the difference between HSI spectral and spatial dimensions, which also hinders the refinement of performance. In order to mitigate these issues, an attention symbiotic neural network (ASNN) based on relative water content (RWC) retrieval (RWCR) is proposed for HSI refined classification in this article. ASNN is a multisupervised deep learning model that is able to extract spectral-spatial and biochemical features from the multilabel input data simultaneously. The augmented multilabel data, consisting of original spectral-spatial labels and RWC labels, are generated in the RWCR inversion model, which contains the proposed spectral index [red edge slope (RES)] calculation and the proposed adaptive grading algorithm. There are two critical parts in ASNN, soft band selection (SBS) module and dimensionality-varied feature extraction (DVFE) module, which are responsible for attention assignment and synergistic spectral-spatial feature extraction, respectively. The experimental results on real HSI data verify the effectiveness of RES, SBS, and DVFE in ablation studies. It is also demonstrated that ASNN has the capacity for improving intraclass and interclass accuracy in refined classification and providing a competitive advantage in comparison with several state-of-the-art methods.