{"title":"SEIS-Net:基于斯温变压器的三维合成孔径雷达增强成像网络","authors":"Yifei Hu;Mou Wang;Shunjun Wei;Jiahui Li;Rong Shen","doi":"10.1109/JSTARS.2024.3472845","DOIUrl":null,"url":null,"abstract":"Conventional 3-D synthetic aperture radar (SAR) sparse imaging algorithms suffer from degradation in weakly sparse scenes due to their reliance on inherent sparsity. In addition, they are constrained by high computational complexity and parametric tuning. To address these problems, we propose a novel 3-D SAR enhanced imaging network based on swin transformer dubbed SEIS-Net. The proposed algorithm consists of two cascaded stages. The first one focuses on estimating the missing measurement elements by constructing a Unet based on the swin transformer. The second stage aims to recover a high-quality image from the estimated echo matrix. The proposed imaging network is theoretically derived from fast iterative shrinkage-thresholding algorithm optimization framework, where the network weights can be learned from an end-to-end training procedure. Finally, simulations and real-measured experiments are carried out. Both visual and quantitative results demonstrate the superiority of the proposed SEIS-Net over the current state-of-the-art algorithms in reconstructing 3-D images from sparsely sampled echoes.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"17 ","pages":"18967-18986"},"PeriodicalIF":4.7000,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705080","citationCount":"0","resultStr":"{\"title\":\"SEIS-Net: A 3-D SAR Enhanced Imaging Network Based on Swin Transformer\",\"authors\":\"Yifei Hu;Mou Wang;Shunjun Wei;Jiahui Li;Rong Shen\",\"doi\":\"10.1109/JSTARS.2024.3472845\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Conventional 3-D synthetic aperture radar (SAR) sparse imaging algorithms suffer from degradation in weakly sparse scenes due to their reliance on inherent sparsity. In addition, they are constrained by high computational complexity and parametric tuning. To address these problems, we propose a novel 3-D SAR enhanced imaging network based on swin transformer dubbed SEIS-Net. The proposed algorithm consists of two cascaded stages. The first one focuses on estimating the missing measurement elements by constructing a Unet based on the swin transformer. The second stage aims to recover a high-quality image from the estimated echo matrix. The proposed imaging network is theoretically derived from fast iterative shrinkage-thresholding algorithm optimization framework, where the network weights can be learned from an end-to-end training procedure. Finally, simulations and real-measured experiments are carried out. Both visual and quantitative results demonstrate the superiority of the proposed SEIS-Net over the current state-of-the-art algorithms in reconstructing 3-D images from sparsely sampled echoes.\",\"PeriodicalId\":13116,\"journal\":{\"name\":\"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\",\"volume\":\"17 \",\"pages\":\"18967-18986\"},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-10-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10705080\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10705080/\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10705080/","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
SEIS-Net: A 3-D SAR Enhanced Imaging Network Based on Swin Transformer
Conventional 3-D synthetic aperture radar (SAR) sparse imaging algorithms suffer from degradation in weakly sparse scenes due to their reliance on inherent sparsity. In addition, they are constrained by high computational complexity and parametric tuning. To address these problems, we propose a novel 3-D SAR enhanced imaging network based on swin transformer dubbed SEIS-Net. The proposed algorithm consists of two cascaded stages. The first one focuses on estimating the missing measurement elements by constructing a Unet based on the swin transformer. The second stage aims to recover a high-quality image from the estimated echo matrix. The proposed imaging network is theoretically derived from fast iterative shrinkage-thresholding algorithm optimization framework, where the network weights can be learned from an end-to-end training procedure. Finally, simulations and real-measured experiments are carried out. Both visual and quantitative results demonstrate the superiority of the proposed SEIS-Net over the current state-of-the-art algorithms in reconstructing 3-D images from sparsely sampled echoes.
期刊介绍:
The IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing addresses the growing field of applications in Earth observations and remote sensing, and also provides a venue for the rapidly expanding special issues that are being sponsored by the IEEE Geosciences and Remote Sensing Society. The journal draws upon the experience of the highly successful “IEEE Transactions on Geoscience and Remote Sensing” and provide a complementary medium for the wide range of topics in applied earth observations. The ‘Applications’ areas encompasses the societal benefit areas of the Global Earth Observations Systems of Systems (GEOSS) program. Through deliberations over two years, ministers from 50 countries agreed to identify nine areas where Earth observation could positively impact the quality of life and health of their respective countries. Some of these are areas not traditionally addressed in the IEEE context. These include biodiversity, health and climate. Yet it is the skill sets of IEEE members, in areas such as observations, communications, computers, signal processing, standards and ocean engineering, that form the technical underpinnings of GEOSS. Thus, the Journal attracts a broad range of interests that serves both present members in new ways and expands the IEEE visibility into new areas.