{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T18:31:55Z","timestamp":1770834715932,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T00:00:00Z","timestamp":1721260800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFA0714100"],"award-info":[{"award-number":["2020YFA0714100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["12171076"],"award-info":[{"award-number":["12171076"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["62135015"],"award-info":[{"award-number":["62135015"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["20210101146JC"],"award-info":[{"award-number":["20210101146JC"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Natural Science Foundation of China","award":["2020YFA0714100"],"award-info":[{"award-number":["2020YFA0714100"]}]},{"name":"National Natural Science Foundation of China","award":["12171076"],"award-info":[{"award-number":["12171076"]}]},{"name":"National Natural Science Foundation of China","award":["62135015"],"award-info":[{"award-number":["62135015"]}]},{"name":"National Natural Science Foundation of China","award":["20210101146JC"],"award-info":[{"award-number":["20210101146JC"]}]},{"name":"Science and Technology Department of Jilin Province","award":["2020YFA0714100"],"award-info":[{"award-number":["2020YFA0714100"]}]},{"name":"Science and Technology Department of Jilin Province","award":["12171076"],"award-info":[{"award-number":["12171076"]}]},{"name":"Science and Technology Department of Jilin Province","award":["62135015"],"award-info":[{"award-number":["62135015"]}]},{"name":"Science and Technology Department of Jilin Province","award":["20210101146JC"],"award-info":[{"award-number":["20210101146JC"]}]},{"name":"the Open Research Fund of KLAS, Northeast Normal University","award":["2020YFA0714100"],"award-info":[{"award-number":["2020YFA0714100"]}]},{"name":"the Open Research Fund of KLAS, Northeast Normal University","award":["12171076"],"award-info":[{"award-number":["12171076"]}]},{"name":"the Open Research Fund of KLAS, Northeast Normal University","award":["62135015"],"award-info":[{"award-number":["62135015"]}]},{"name":"the Open Research Fund of KLAS, Northeast Normal University","award":["20210101146JC"],"award-info":[{"award-number":["20210101146JC"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Inverse synthetic aperture LiDAR (ISAL) can create high-resolution images within a few milliseconds, which are employed for long-range airspace target identification. However, its optical signal characteristics incur the non-negligible higher-order kinematic parameters of the target and phase errors due to atmospheric turbulence. These higher-order parameters and phase errors make it challenging for imaging the ISAL signals. In this paper, we propose an approach integrating the RD algorithm with an image translation network. Unlike the conventional methods, our approach does not require high accuracy in estimating each target motion and atmospheric parameter. The phase error of the RD image is fitted by an image translation network, which greatly simplifies the computational difficulty of the ISAL imaging model. The experimental results demonstrate that our model has good generalization performance. Specifically, our method consistently performs well in capturing the target information under different types of noise and sparsity aperture (SA) rates compared to other conventional methods. In addition, our approach can be applied to the measured data after training the network by using simulated data.<\/jats:p>","DOI":"10.3390\/rs16142635","type":"journal-article","created":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T16:51:12Z","timestamp":1721321472000},"page":"2635","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Improved ISAL Imaging Based on RD Algorithm and Image Translation Network Cascade"],"prefix":"10.3390","volume":"16","author":[{"given":"Jiarui","family":"Li","sequence":"first","affiliation":[{"name":"Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130024, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5084-8590","authenticated-orcid":false,"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Xiaofei","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Applied Statistics of MOE, School of Mathematics and Statistics, Northeast Normal University, Changchun 130024, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"129482","DOI":"10.1016\/j.optcom.2023.129482","article-title":"Low sampling rate digital dechirp for Inverse Synthetic Aperture Ladar imaging processing","volume":"540","author":"Hong","year":"2023","journal-title":"Opt. 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