Yanyan Lv;Dan Li;Fanqiang Kong;Xinwei Wan;Qiang Wang
{"title":"基于光滑低秩度联合梯度稀疏度增强高光谱图像压缩感知重构","authors":"Yanyan Lv;Dan Li;Fanqiang Kong;Xinwei Wan;Qiang Wang","doi":"10.1109/JSTARS.2025.3580668","DOIUrl":null,"url":null,"abstract":"The application of compressive sensing (CS) theory in hyperspectral images (HSI) reconstruction has been validated. The key to model-based reconstruction methods lies in effectively integrating prior knowledge of HSI. However, capturing multiple prior knowledge means that multiple regularization terms need to be set, which not only increases the complexity of the model, but also reduces its stability. In this article, a model based on smooth low-rank joint gradient sparsity is proposed to enhance the capability of HSI compressed sensing reconstruction. First, we propose a new model called the smooth spatial-spectral low-rank model (SSLR). Unlike most current models that treat the low-rankness and local smoothness of HSI as two separate regularization terms, SSLR uses only one regularization term. In addition, the use of 2-D gradient images introduces spatial–spectral correlation, while the constraint of Tucker rank allows for a more comprehensive capture of low-rank information across spatial and spectral dimensions. At the same time, to address the shortcomings of SSLR in capturing spatial features and sparsity, we design the multidimensional coupled gradient sparsity model to extract these features. The combination of 1-D spatial gradient images with a 2-D spatial-spectral gradient image fully captures the gradient sparsity across multiple dimensions. In addition, it obtains the rich spatial structure information of HSI. The superiority of the proposed model is demonstrated through comparative experiments conducted on three datasets.","PeriodicalId":13116,"journal":{"name":"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing","volume":"18 ","pages":"15659-15674"},"PeriodicalIF":4.7000,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11042909","citationCount":"0","resultStr":"{\"title\":\"Enhancing Hyperspectral Images Compressive Sensing Reconstruction With Smooth Low-Rankness Joint Gradient Sparsity\",\"authors\":\"Yanyan Lv;Dan Li;Fanqiang Kong;Xinwei Wan;Qiang Wang\",\"doi\":\"10.1109/JSTARS.2025.3580668\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The application of compressive sensing (CS) theory in hyperspectral images (HSI) reconstruction has been validated. The key to model-based reconstruction methods lies in effectively integrating prior knowledge of HSI. However, capturing multiple prior knowledge means that multiple regularization terms need to be set, which not only increases the complexity of the model, but also reduces its stability. In this article, a model based on smooth low-rank joint gradient sparsity is proposed to enhance the capability of HSI compressed sensing reconstruction. First, we propose a new model called the smooth spatial-spectral low-rank model (SSLR). Unlike most current models that treat the low-rankness and local smoothness of HSI as two separate regularization terms, SSLR uses only one regularization term. In addition, the use of 2-D gradient images introduces spatial–spectral correlation, while the constraint of Tucker rank allows for a more comprehensive capture of low-rank information across spatial and spectral dimensions. At the same time, to address the shortcomings of SSLR in capturing spatial features and sparsity, we design the multidimensional coupled gradient sparsity model to extract these features. The combination of 1-D spatial gradient images with a 2-D spatial-spectral gradient image fully captures the gradient sparsity across multiple dimensions. In addition, it obtains the rich spatial structure information of HSI. The superiority of the proposed model is demonstrated through comparative experiments conducted on three datasets.\",\"PeriodicalId\":13116,\"journal\":{\"name\":\"IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing\",\"volume\":\"18 \",\"pages\":\"15659-15674\"},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2025-06-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11042909\",\"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/11042909/\",\"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/11042909/","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}
The application of compressive sensing (CS) theory in hyperspectral images (HSI) reconstruction has been validated. The key to model-based reconstruction methods lies in effectively integrating prior knowledge of HSI. However, capturing multiple prior knowledge means that multiple regularization terms need to be set, which not only increases the complexity of the model, but also reduces its stability. In this article, a model based on smooth low-rank joint gradient sparsity is proposed to enhance the capability of HSI compressed sensing reconstruction. First, we propose a new model called the smooth spatial-spectral low-rank model (SSLR). Unlike most current models that treat the low-rankness and local smoothness of HSI as two separate regularization terms, SSLR uses only one regularization term. In addition, the use of 2-D gradient images introduces spatial–spectral correlation, while the constraint of Tucker rank allows for a more comprehensive capture of low-rank information across spatial and spectral dimensions. At the same time, to address the shortcomings of SSLR in capturing spatial features and sparsity, we design the multidimensional coupled gradient sparsity model to extract these features. The combination of 1-D spatial gradient images with a 2-D spatial-spectral gradient image fully captures the gradient sparsity across multiple dimensions. In addition, it obtains the rich spatial structure information of HSI. The superiority of the proposed model is demonstrated through comparative experiments conducted on three datasets.
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.