Semisupervised Spatial-Spectral Feature Extraction With Attention Mechanism for Hyperspectral Image Classification

Deep learning-based methods have demonstrated their competitive classification performance with sufficient labeled training samples. However, in practical hyperspectral image (HSI) classification applications, the labeled samples available for training are extremely limited compared with a large amount of unlabeled data, because the expert annotation of HSI is labor-intensive and time-consuming. To address the abovementioned issues, an end-to-end framework called semisupervised spatial-spectral dual-path networks ((SDPN)-D-3) is proposed to learn discriminative spatial-spectral features from limited labeled data and abundant unlabeled data. Unlike many semisupervised deep learning methods that require to produce pseudo-labels (cluster labels), an unsupervised branch of (SDPN)-D-3 can directly extract deep representations from unlabeled samples, and it utilizes octave convolution (Oct-Conv) to simultaneously mine local detail features and global contextual information of unlabeled samples. (SDPN)-D-3 improves classification results by exploring the fusion features to reconstruct supervised and unsupervised features in turn. Furthermore, a spatial-spectral attention mechanism is employed to take full advantage of supervised features to selectively emphasize effective unsupervised representations and suppress useless ones. Experimental results on three real HSI datasets demonstrate the superior classification performance of the proposed (SDPN)-D-3 compared with many state-of-the-art (SOTA) methods.