{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T08:32:54Z","timestamp":1773909174142,"version":"3.50.1"},"reference-count":55,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T00:00:00Z","timestamp":1652659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Fund for Distinguished Young Scholars","award":["61925112"],"award-info":[{"award-number":["61925112"]}]},{"name":"National Science Fund for Distinguished Young Scholars","award":["2020KJXX-091"],"award-info":[{"award-number":["2020KJXX-091"]}]},{"name":"National Science Fund for Distinguished Young Scholars","award":["2020TD-015"],"award-info":[{"award-number":["2020TD-015"]}]},{"name":"National Science Fund for Distinguished Young Scholars","award":["2022JQ-693"],"award-info":[{"award-number":["2022JQ-693"]}]},{"name":"Innovation Capability Support Program of Shaanxi","award":["61925112"],"award-info":[{"award-number":["61925112"]}]},{"name":"Innovation Capability Support Program of Shaanxi","award":["2020KJXX-091"],"award-info":[{"award-number":["2020KJXX-091"]}]},{"name":"Innovation Capability Support Program of Shaanxi","award":["2020TD-015"],"award-info":[{"award-number":["2020TD-015"]}]},{"name":"Innovation Capability Support Program of Shaanxi","award":["2022JQ-693"],"award-info":[{"award-number":["2022JQ-693"]}]},{"name":"Shaanxi Natural Science Basic Research Program","award":["61925112"],"award-info":[{"award-number":["61925112"]}]},{"name":"Shaanxi Natural Science Basic Research Program","award":["2020KJXX-091"],"award-info":[{"award-number":["2020KJXX-091"]}]},{"name":"Shaanxi Natural Science Basic Research Program","award":["2020TD-015"],"award-info":[{"award-number":["2020TD-015"]}]},{"name":"Shaanxi Natural Science Basic Research Program","award":["2022JQ-693"],"award-info":[{"award-number":["2022JQ-693"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Ship detection is an important research topic in the field of remote sensing. Compared with optical detection methods, Synthetic Aperture Radar (SAR) ship detection can penetrate clouds to detect hidden ships in all-day and all-weather. Currently, the state-of-the-art methods exploit convolutional neural networks to train ship detectors, which require a considerable labeled dataset. However, it is difficult to label the SAR images because of expensive labor and well-trained experts. To address the above limitations, this paper explores a cross-domain ship detection task, which adapts the detector from labeled optical images to unlabeled SAR images. There is a significant visual difference between SAR images and optical images. To achieve cross-domain detection, the multi-level alignment network, which includes image-level, convolution-level, and instance-level, is proposed to reduce the large domain shift. First, image-level alignment exploits generative adversarial networks to generate SAR images from the optical images. Then, the generated SAR images and the real SAR images are used to train the detector. To further minimize domain distribution shift, the detector integrates convolution-level alignment and instance-level alignment. Convolution-level alignment trains the domain classifier on each activation of the convolutional features, which minimizes the domain distance to learn domain-invariant features. Instance-level alignment reduces domain distribution shift on the features extracted from the region proposals. The entire multi-level alignment network is trained end-to-end and its effectiveness is proved on multiple cross-domain ship detection datasets.<\/jats:p>","DOI":"10.3390\/rs14102389","type":"journal-article","created":{"date-parts":[[2022,5,16]],"date-time":"2022-05-16T21:36:06Z","timestamp":1652736966000},"page":"2389","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Multi-Level Alignment Network for Cross-Domain Ship Detection"],"prefix":"10.3390","volume":"14","author":[{"given":"Chujie","family":"Xu","sequence":"first","affiliation":[{"name":"Key Laboratory of Spectral Imaging Technology CAS, Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Xiangtao","family":"Zheng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spectral Imaging Technology CAS, Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"}]},{"given":"Xiaoqiang","family":"Lu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Spectral Imaging Technology CAS, Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,5,16]]},"reference":[{"key":"ref_1","first-page":"5217712","article-title":"A Robust One-Stage Detector for Multiscale Ship Detection with Complex Background in Massive SAR Images","volume":"60","author":"Yang","year":"2022","journal-title":"IEEE Trans. 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