Adaptive Fusion and Mask Refinement Instance Segmentation Network for High Resolution Remote Sensing Images

Instance segmentation of remote sensing images (RSIs) is an active yet challenging task because of the huge scale variation and arbitrary complex shapes of objects. To address these issues, we propose an adaptive fusion and mask refinement (AFMR) instance segmentation network for RSIs in this paper. More precisely, AFMR consists of an adaptive fusion module to learn multi-scale complementary spatial features in an unsupervised manner, and a content-aware module for segmentation mask refinement. These two modules enable a better feature learning of convolutional neural network and boost the performance by 1.5% compared with the baseline method. In iSAID, a large-scale dataset for RSIs instance segmentation, our AFMR framework achieves the state-of-the-art accuracy, which verifies the superiority of the proposed method.