{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T13:42:05Z","timestamp":1773927725129,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T00:00:00Z","timestamp":1729036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62271248"],"award-info":[{"award-number":["62271248"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["BK20230090"],"award-info":[{"award-number":["BK20230090"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["KLSMNR-K202303"],"award-info":[{"award-number":["KLSMNR-K202303"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["62271248"],"award-info":[{"award-number":["62271248"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["BK20230090"],"award-info":[{"award-number":["BK20230090"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["KLSMNR-K202303"],"award-info":[{"award-number":["KLSMNR-K202303"]}]},{"name":"Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources of China","award":["62271248"],"award-info":[{"award-number":["62271248"]}]},{"name":"Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources of China","award":["BK20230090"],"award-info":[{"award-number":["BK20230090"]}]},{"name":"Key Laboratory of Land Satellite Remote Sensing Application, Ministry of Natural Resources of China","award":["KLSMNR-K202303"],"award-info":[{"award-number":["KLSMNR-K202303"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Sparse synthetic aperture radar (SAR) imaging has demonstrated excellent potential in image quality improvement and data compression. However, conventional observation matrix-based methods suffer from high computational overhead, which is hard to use for real data processing. The approximated observation sparse SAR imaging method relieves the computation pressure, but it needs to manually set the parameters to solve the optimization problem. Thus, several deep learning (DL) SAR imaging methods have been used for scene recovery, but many of them employ dual-path networks. To better leverage the complex-valued characteristics of echo data, in this paper, we present a novel complex-valued convolutional neural network (CNN)-based approximated observation sparse SAR imaging method, which is a single-path DL network. Firstly, we present the approximated observation-based model via the chirp-scaling algorithm (CSA). Next, we map the process of the iterative soft thresholding (IST) algorithm into the deep network form, and design the symmetric complex-valued CNN block to achieve the sparse recovery of large-scale scenes. In comparison to matched filtering (MF), the approximated observation sparse imaging method, and the existing DL SAR imaging methods, our complex-valued network model shows excellent performance in image quality improvement especially when the used data are down-sampled.<\/jats:p>","DOI":"10.3390\/rs16203850","type":"journal-article","created":{"date-parts":[[2024,10,16]],"date-time":"2024-10-16T10:11:04Z","timestamp":1729073464000},"page":"3850","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning-Based Approximated Observation Sparse SAR Imaging via Complex-Valued Convolutional Neural Network"],"prefix":"10.3390","volume":"16","author":[{"given":"Zhongyuan","family":"Ji","sequence":"first","affiliation":[{"name":"College of Electronic Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"},{"name":"College of Criminal Justice, Shandong University of Political Science and Law, Jinan 250014, China"},{"name":"The Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Eduction, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}]},{"given":"Lingyu","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"},{"name":"The Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Eduction, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9357-8412","authenticated-orcid":false,"given":"Hui","family":"Bi","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"},{"name":"The Key Laboratory of Radar Imaging and Microwave Photonics, Ministry of Eduction, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1722","DOI":"10.1007\/s11432-012-4633-4","article-title":"Sparse microwave imaging: Principles and applications","volume":"55","author":"Zhang","year":"2012","journal-title":"Sci. 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