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This paper proposes a correlation-based joint CFAR detector using adaptively-truncated statistics (hereafter called TS-2DLNCFAR) in SAR images. The proposed joint CFAR detector exploits the gray intensity correlation characteristics by building a two-dimensional (2D) joint log-normal model as the joint distribution (JPDF) of the clutter, so joint CFAR detection is realized. Inspired by the CFAR detection methodology, we design an adaptive threshold-based clutter truncation method to eliminate the high-intensity outliers, such as interfering ship targets, side-lobes, and ghosts in the background window, whereas the real clutter samples are preserved to the largest degree. A 2D joint log-normal model is accurately built using the adaptively-truncated clutter through simple parameter estimation, so the joint CFAR detection performance is greatly improved. Compared with traditional CFAR detectors, the proposed TS-2DLNCFAR detector achieves a high PD and a low false alarm rate (FAR) in multiple target situations. The superiority of the proposed TS-2DLNCFAR detector is validated on the multi-look Envisat-ASAR and TerraSAR-X data.<\/jats:p>","DOI":"10.3390\/s17040686","type":"journal-article","created":{"date-parts":[[2017,3,27]],"date-time":"2017-03-27T10:49:10Z","timestamp":1490611750000},"page":"686","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Correlation-Based Joint CFAR Detector Using Adaptively-Truncated Statistics in SAR Imagery"],"prefix":"10.3390","volume":"17","author":[{"given":"Jiaqiu","family":"Ai","sequence":"first","affiliation":[{"name":"School of Computer and Information, Hefei University of Technology, Tunxi Road, Hefei 230009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuezhi","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Hefei University of Technology, Tunxi Road, Hefei 230009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1030-510X","authenticated-orcid":false,"given":"Fang","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Hefei University of Technology, Tunxi Road, Hefei 230009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhangyu","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Hefei University of Technology, Tunxi Road, Hefei 230009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lu","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Computer and Information, Hefei University of Technology, Tunxi Road, Hefei 230009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9882-351X","authenticated-orcid":false,"given":"He","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Yudao Avenue, Nanjing 210016, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,3,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Leng, X., Ji, K., Zhou, S., Xing, X., and Zou, H. 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