Semisupervised Center Loss for Remote Sensing Image Scene Classification

Abstract

High-resolution remote sensing image scene classification is a scene-level classification task. Driven by a wide range of applications, accurate scene annotation has become a hot and challenging research topic. In recent years, convolutional neural networks (ConvNets) have achieved promising performance among a variety of supervised classification methods. However, due to the lack of clearly labeled remote sensing images, it may be difficult to further improve the performance of scene classification. To address this issue, we propose a novel semisupervised center loss for scene classification. The main innovation of our method is to develop a cooperative framework of supervised and unsupervised branches in an end-to-end way. Specifically, we consider the class centers as guiding factors between the supervised and unsupervised branches. The supervised branch relies on a small number of labeled samples to generate class centers, which serve as initialization centers for the unsupervised branch. Meanwhile, the unsupervised branch utilizes the easily available remote sensing images to correct the class centers for enhancing the discriminative power of supervised ConvNets. Experimental results on three public benchmarks have indicated that the proposed method is superior to supervised center loss based methods.