{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T18:03:22Z","timestamp":1774029802237,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2024,9,28]],"date-time":"2024-09-28T00:00:00Z","timestamp":1727481600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62071041"],"award-info":[{"award-number":["62071041"]}]},{"name":"National Natural Science Foundation of China","award":["61701554"],"award-info":[{"award-number":["61701554"]}]},{"name":"National Natural Science Foundation of China","award":["52374169"],"award-info":[{"award-number":["52374169"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Image registration is essential for applications requiring the joint processing of inverse synthetic aperture radar (ISAR) images, such as interferometric ISAR, image enhancement, and image fusion. Traditional image registration methods, developed for optical images, often perform poorly with ISAR images due to their differing imaging mechanisms. This paper introduces a novel spatial feature-based ISAR image registration method. The method encodes spatial information by utilizing the distances and angles between dominant scatterers to construct translation and rotation-invariant feature descriptors. These feature descriptors are then used for scatterer matching, while the coordinate transformation of matched scatterers is employed to estimate image registration parameters. To mitigate the glint effects of scatterers, the random sample consensus (RANSAC) algorithm is applied for parameter estimation. By extracting global spatial information, the constructed feature curves exhibit greater stability and reliability. Additionally, using multiple dominant scatterers ensures adaptability to low signal-to-noise (SNR) ratio conditions. The effectiveness of the method is validated through both simulated and natural ISAR image sequences. Comparative performance results with traditional image registration methods, such as the SIFT, SURF and SIFT+SURF algorithms, are also included.<\/jats:p>","DOI":"10.3390\/rs16193625","type":"journal-article","created":{"date-parts":[[2024,9,30]],"date-time":"2024-09-30T05:45:27Z","timestamp":1727675127000},"page":"3625","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Spatial Feature-Based ISAR Image Registration for Space Targets"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7216-9014","authenticated-orcid":false,"given":"Lizhi","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Information Engineering, Minzu University of China, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7158-0688","authenticated-orcid":false,"given":"Junling","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7626-0577","authenticated-orcid":false,"given":"Jiaoyang","family":"Su","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Minzu University of China, Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-8575-0287","authenticated-orcid":false,"given":"Haoyue","family":"Luo","sequence":"additional","affiliation":[{"name":"China Aerospace Science and Industry Corp Second Research Institute, Institute No.25 of the Second Academy, Beijing 100854, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"113917","DOI":"10.1109\/ACCESS.2021.3104799","article-title":"Inverse Synthetic Aperture Radar Imaging: A Historical Perspective and State-of-the-Art Survey","volume":"9","author":"Vehmas","year":"2021","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"MacDonald, M., Abouzahra, M., and Stambaugh, J. 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