Weakly supervised object extraction with iterative contour prior for remote sensing images
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RESEARCH
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Weakly supervised object extraction with iterative contour prior for remote sensing images Chu He1,2* , Yu Zhang1 , Bo Shi1 , Xin Su3 , Xin Xu1 and Mingsheng Liao2
Abstract 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. 1 Introduction Object detection and segmentation have received considerable attention as important procedures in automatic object identification in such fields as computer vision, remote sensing image processing, and so on. Based on the large number of works to which object detection and segmentation have been applied, a key distinction between these two methods can be found; object segmentation is usually interactive and incorporates guidance from the user throughout the analysis process, such as in GraphCut [1] and Snake [2], whereas object detection needs learning samples and/or supervising information from the user at the beginning of the analysis, such as in latent support vector machine LSVMs [3], Wu et al.’s Active Basis [4]. Nevertheless, object detection and object segmentation share numerous theoretical and methodological features, which if explored will be of benefit to each other. In this article, object detection results based on Active Basis [4] are developed to replace supervised learning samples in object segmentation. Object segmentation results can be obtained by providing several object samples. Furthermore, this combination employs a contour prior model *Correspondence: [email protected] 1 School of Electronic Information, Wuhan University, Wuhan 430079, P.R. China 2 The State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China Full list of author information is available at the end of the article
based on the detection results, thereby improving the segmentation performance. From a methodological perspective, the main idea of numerous methods that have recently been used for object detection and segmentation can be divided into shape-based methods and feature-based methods. Shapebased methods, such as Felzenszwalb et al.’s LSVMs [3], Wu et al.’s Active Basis [4], Laptev et al.’s Snake [2], and Ferrari’s kAS [5], exploit shapes similarities between objects by using different strategies and then obtain segmentation results through by connecting the segment
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