Distillation Remote Sensing Object Counting via Multi-Scale Context Feature Aggregation

Abstract

Remote sensing object counting is an important issue in remote sensing analysis. Remote sensing object counting has many challenges, such as large-scale variations and complex backgrounds. The previous counting methods have many shortboards, such as only focusing on local appearance features of target scenes and ignoring the self-supervision ability of the network itself. To remedy the above problems, in this article, we propose a novel remote sensing object counting method, which contains the adaptive multi-scale context aggregation module (AMCAM) and the self-context distillation module (SCDM). The AMCAM can model and fuse context information from different receptive fields effectively. It also keeps detailed information through multiple pixel attention (PA) modules step by step. The SCDM can improve the representation learning without adding any additional supervision information. SCDM uses feature maps from the deeper layer of the network to supervise feature maps from the earlier layer of the network. Our method has achieved good performance on the remote sensing object counting dataset, RSOC, and mainstream crowd counting datasets, such as ShanghaiTech and UCF-QNRF datasets.