An Elliptic Centerness for Object Instance Segmentation in Aerial Images

Abstract

Instance segmentation in aerial images is an important and challenging task. Most of the existing methods have adapted instance segmentation algorithms developed for natural images to aerial images. However, these methods easily suffer from performance degradation in aerial images, due to the scale variations, large aspect ratios, and arbitrary orientations of instances caused by the bird’s-eye view of aerial images. To address this issue, we propose an elliptic centerness (EC) for instance segmentation in aerial images, which can assign the proper centerness values to the intricate aerial instances and thus mitigate the performance degradation. Specifically, we introduce ellipses to fit the various contours of aerial instances and measure these fitted ellipses by two-dimensional anisotropic Gaussian distribution. Armed with EC, we develop a one-stage aerial instance segmentation network. Extensive experiments on a commonly used dataset, the instance segmentation in aerial images dataset (iSAID), demonstrate that our proposed method can achieve a remarkable performance of instance segmentation while introducing negligible computational cost.