{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:49:21Z","timestamp":1760230161758,"version":"build-2065373602"},"reference-count":62,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T00:00:00Z","timestamp":1657584000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["61905240"],"award-info":[{"award-number":["61905240"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Ship detection in visible remote sensing (VRS) images has been widely used in the military and civil fields. However, the various backgrounds and the variable scale and orientation bring great difficulties to effective detection. In this paper, we propose a novel ship target detection scheme based on small training samples. The scheme contains two main stages: candidate region extraction and ship identification. In the first stage, we propose a visual saliency detection model based on the difference in covariance statistical characteristics to quickly locate potential ships. Moreover, the multi-scale fusion for the saliency model is designed to overcome the problem of scale variation. In the second stage, we propose a three-channel aggregate feature, which combines a rotation-invariant histogram of oriented gradient and the circular frequency feature. The feature can identify the ship target well by avoiding the impact of its rotation and shift. Finally, we propose the VRS ship dataset that contains more realistic scenes. The results on the VRS ship dataset demonstrate that the saliency model achieves the best AUC value with 0.9476, and the overall detection achieves a better performance of 65.37% in terms of AP@0.5:0.95, which basically meets the need of the detection tasks.<\/jats:p>","DOI":"10.3390\/rs14143347","type":"journal-article","created":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T03:50:36Z","timestamp":1657597836000},"page":"3347","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Ship Detection in Visible Remote Sensing Image Based on Saliency Extraction and Modified Channel Features"],"prefix":"10.3390","volume":"14","author":[{"given":"Yang","family":"Tian","sequence":"first","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Jinghong","family":"Liu","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6752-5811","authenticated-orcid":false,"given":"Shengjie","family":"Zhu","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Fang","family":"Xu","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}]},{"given":"Guanbing","family":"Bai","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}]},{"given":"Chenglong","family":"Liu","sequence":"additional","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,12]]},"reference":[{"key":"ref_1","unstructured":"Li, J., Tian, J., Gao, P., and Li, L. 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