Single-Object-Based Region Growth: Key Area Localization Model for Remote Sensing Image Scene Classification

Abstract

Remote sensing image scene classification is a challenging task due to the large differences within the same classes and a large number of similar scenes among different classes. To tackle this problem, this paper proposes a single-object-based region growth algorithm to effectively localize the most key area in the whole image, so as to generate more discriminative local fine-grained features for the image scene. Concurrently, a local-global two-branch network is designed to utilize the features of the images from multiple perspectives, respectively. Specially, the global branch extracts global features (such as contour, texture) from the whole image, and local branch extracts more local features from the local key area. Finally, the global and local classification scores are integrated to make the final decision. Experiments are performed on three publicly available data sets, and the results show that this method can achieve higher accuracy compared to most existing state-of-the-art methods.