A Comprehensive Survey of Optical Remote Sensing Image Segmentation Methods. Issue 5 (2nd September 2020)

• Record Type: Journal Article
• Authors: Wang, Yongzhi; Lv, Hua; Deng, Rui; Zhuang, Shengbing
• Is Part Of: Canadian journal of remote sensing. Volume 46:Issue 5(2020)
• Page Start: 501
• Page End: 531
• Publication Date: 2020-09-02
• Subjects: Remote sensing -- Periodicals; 621.367805
• DOI: 10.1080/07038992.2020.1805729
• Languages: English
• ISSNs: 0703-8992

Abstract

Abstract: Many papers have reviewed remote sensing image segmentation (RSIS) algorithms currently. Those existing surveys are insufficiently exhaustive to sort out the various RSIS methods, it is impossible to comprehensively compare characteristics of different RSIS methods. In addition, the segmentation efficiency and accuracy of the RSIS methods cannot always meet the subsequent image analysis requirements. Thus, a clear comparative analysis of various RSIS methods is essential to provide an in-depth understanding of RSIS and theoretical ideas for conducting in-depth research in the future. The goal of this article is to provide readers with the latest information on optical RSIS technology. Comparative measures of these methods are provided in terms of conceptual details, the merits and demerits, and the performance of various RSIS methods. Moreover, various RSIS methods' experiments are carried out on optical images using the NWPU VHR-10 public remote sensing datasets. Through the review of optical RSIS methods, this paper provides data as complete as possible for further related research and development of RSIS methods.