Weakly supervised object extraction with iterative contour prior for remote sensing images

This article presents a weakly supervised approach based on Markov random field model for the extraction of objects (e.g., aircrafts) in optical remote sensing images. This approach is capable of localizing and then segmenting objects in optical remote sensing images by relying only on several object samples without artificial labels. However, unlike direct combinations of object detection and segmentation, the proposed method develops a contour prior model based on detection results, thereby improving segmentation performance. Furthermore, we iteratively update the contour prior information based on the expectation-maximization algorithm. Numerical experiments illustrate that the proposed method can successfully be applied to the extraction of aircrafts in optical remote sensing images.