SCL-Net: An End-to-End Supervised Contrastive Learning Network for Hyperspectral Image Classification.

In recent years, deep learning (DL) presents a promising performance in hyperspectral image (HSI) classification, due to the powerful capability of automatically learning deep semantic characteristics of images. However, it is still difficult to learn highly discriminative features when limited samples are available for training a deep network. Focused on this issue, a novel end-to-end supervised contrastive learning network (SCL-Net) for spectral-spatial classification is proposed, in this article. Instead of learning features of the individual sample, the supervised contrastive learning is introduced to capture the similarity and dissimilarity distribution properties of sample pairs in a feature representation space. In this way, the need for plenty of training samples will be alleviated while an effective network training mechanism is provided for learning highly separative features. Here, SCL-Net mainly consists of one pairwise contrastive learning (PCL) subnetwork and one multilevel spectral-spatial information fusion (MLSIF) subnetwork. For the PCL subnetwork, spectral vectors are projected into deep spectral features based on convolutional operators, which are then followed by distance evaluation between "positive " pairs of similar samples and "negative " pairs of dissimilar ones. Then, a spectral distance matrix is constructed to push the network to gradually learn better features of higher intraclass compactness and interclass dispersion. For the MLSIF subnetwork, a hybrid feature-decision fusion strategy is designed, where spatial and spectral features are jointly exploited to further boost the classification performance. In specific, feature fusion is conducted by connecting low/mid/high-level spectral and spatial features via weighting, while multiple class estimations based on multilevel fusion features are adaptively integrated via probabilistic decision fusion. Overall, these two subnetworks are collaboratively trained in one framework, by optimizing a defined joint loss function consisting of a contrastive loss and a cross-entropy loss. Compared with several state-of-the-art methods, the proposed method yields a superior classification performance in terms of both objective metrics and visual performance.