Generalized Category Discovery for Remote Sensing Image Scene Classification

Deep neural networks have achieved promising progress in remote sensing (RS) image classification. However, the training process requires abundant samples for each class, and it is unrealistic to annotate labels for each RS category, especially considering that the RS target database is increasing dynamically. Therefore, we introduce an innovative prototype network tailored for Generalized Category Discovery (GCD) in remote sensing scene classification. This network consists of two essential modules: one dedicated to representation learning and the other to prototype learning. Through extensive experiments conducted on three benchmark datasets, i.e., RSS-DIVCS, NWPU-RESISC45, and AID, we demonstrate that the proposed model achieves remarkable performance gain up to 20%, effectively addressing the challenges inherent in classifying dynamically varying remote sensing images.