Self-Supervised Learning With Prediction of Image Scale and Spectral Order for Hyperspectral Image Classification
Xiaofei Yang, Weijia Cao, Yao Lu, Yicong Zhou
Abstract
In recent years, Convolutional Neural Networks (CNNs) have achieved great success in hyperspectral image classification attributed to their unparalleled capacity to extract the local information. However, to successfully learn the high-level semantic image features, they always require massive amounts of manually labeled data during the training process, which is expensive, scarce, and impractical, and severely hinders the improvement of supervised deep learning methods. To alleviate these burdens, we present Self-Supervised Learning methods for hyperspectral image classification by a pre-training model using extensive unlabeled data and fine-tuning the hyperspectral image target classification. In this paper, we propose a new method for learning image characteristics by training a CNN to recognize the image scale that is applied to the hyperspectral images (HSIs). In addition, we propose a multi-pretext task method to learn stable and good feature representations combing two different pretext task methods and contrastive loss function. We evaluate the proposed methods in Self-Supervised Learning benchmarks on four benchmark HSIs datasets. The experiment results demonstrate that the proposed methods outperform the traditional supervised deep learning methods when large amounts of unlabeled HSIs data are used. Moreover, it demonstrates that the Self-Supervised Learning method is promising to alleviate dependence on manually labeled data of hyperspectral image classification. Finally, our research contributes to the creation and refinement of Self-Supervised Learning methods for pretextual tasks within the HSIs community.