Three-dimensional Softmax Mechanism Guided Bidirectional GRU Networks for Hyperspectral Remote Sensing Image Classification

Hyperspectral data is a valuable source of both spectral and spatial information. However, to enhance the classification accuracy of hyperspectral image features, it is crucial to capture the spatial spectral features of image elements. The recent years have witnessed the potentials of deep learning methods have shown great promise in the hyperspectral image classification due to their ability to model complex structures and extract multiple features in an end-to-end fashion. Since hyperspectral images can be viewed as sequential data, we propose a novel three-dimensional Softmax mechanism-guided bidirectional GRU network (TDS-BiGRU) for HSI classification. By utilizing a bidirectional GRU to process the sequence data, our method can significantly reduce the processing time. Furthermore, the proposed three-dimensional Softmax mechanism leverages three branches to capture cross-latitude interactions and calculate Softmax weights, which enables us to obtain deeper features with greater discriminative power. The experimental results demonstrate that the proposed method outperforms several prevalent algorithms on four hyperspectral remote sensing datasets. Additionally, we conduct thorough comparisons and ablation tests, which further confirm the effectiveness of our approach. & COPY; 2023 Elsevier B.V. All rights reserved.

Automated summary

In order to enhance the classification accuracy of hyperspectral image features, it is crucial to capture the spatial spectral features. Deep learning methods have shown great promise in hyperspectral image classification due to their ability to model complex structures. In this study, a novel three-dimensional Softmax mechanism-guided bidirectional GRU network (TDS-BiGRU) is proposed for HSI classification, which outperforms several prevalent algorithms on four hyperspectral remote sensing datasets.