Self-Supervised Edge Perceptual Learning Framework for High-Resolution Remote Sensing Images Classification

Abstract

Self-supervised learning (SSL) has been successfully applied to remote sensing image classification by designing pretext tasks to extract valuable feature representations of targets. However, existing SSL methodologies overlook the edge information integral to ground objects, culminating in frequent misclassifications at target boundaries. Additionally, the scarcity of training samples often restricts the full utilization of the knowledge encapsulated in the pre-training model. To address these issues, we propose a novel self-supervised edge perception learning framework (SEPLF) to improve the classification performance of high-resolution remote sensing images (HRSI). The framework comprises self-supervised edge perception learning (SEPL) and training sample augmentation (TSA) algorithms. On the one hand, the SEPL approach leverages morphological data enhancement strategies to render the extracted invariant features more robust. It also effectively mines the potential information concealed at target edges, augmenting ground objects's edge separability. On the other hand, the TSA algorithm not only obtains a large number of training samples but also enhances the intra-class diversity of the samples by considering different spectral features of the same category of ground objects. Experimental results validate that our proposed method outperforms state-of-the-art algorithms, particularly with limited labeled samples.