Remote Sensing Image of The Landsat 8–9 Compressive Sensing via Non-Local Low-Rank Regularization with the Laplace Function
Utilizing low-rank prior data in compressed sensing (CS) schemes for Landsat 8–9 remote sensing images (RSIs) has recently received widespread attention. Nevertheless, most CS algorithms focus on the sparsity of an RSI and ignore its low-rank (LR) nature. Therefore, this paper proposes a new CS reco...
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MDPI AG
2023-03-01
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| author | Guibing Li Weidong Jin Jiaqing Miao Ying Tan Yingling Li Weixuan Zhang Liang Li |
| author_facet | Guibing Li Weidong Jin Jiaqing Miao Ying Tan Yingling Li Weixuan Zhang Liang Li |
| author_sort | Guibing Li |
| collection | DOAJ |
| description | Utilizing low-rank prior data in compressed sensing (CS) schemes for Landsat 8–9 remote sensing images (RSIs) has recently received widespread attention. Nevertheless, most CS algorithms focus on the sparsity of an RSI and ignore its low-rank (LR) nature. Therefore, this paper proposes a new CS reconstruction algorithm for Landsat 8–9 remote sensing images based on a non-local optimization framework (NLOF) that is combined with non-convex Laplace functions (NCLF) used for the low-rank approximation (LAA). Since the developed algorithm is based on an approximate low-rank model of the Laplace function, it can adaptively assign different weights to different singular values. Moreover, exploiting the structural sparsity (SS) and low-rank (LR) between the image patches enables the restored image to obtain better CS reconstruction results of Landsat 8–9 RSI than the existing models. For the proposed scheme, first, a CS reconstruction model is proposed using the non-local low-rank regularization (NLLRR) and variational framework. Then, the image patch grouping and Laplace function are used as regularization/penalty terms to constrain the CS reconstruction model. Finally, to effectively solve the rank minimization problem, the alternating direction multiplier method (ADMM) is used to solve the model. Extensive numerical experimental results demonstrate that the non-local variational framework (NLVF) combined with the low-rank approximate regularization (LRAR) method of non-convex Laplace function (NCLF) can obtain better reconstruction results than the more advanced image CS reconstruction algorithms. At the same time, the model preserves the details of Landsat 8–9 RSIs and the boundaries of the transition areas. |
| first_indexed | 2024-03-11T06:35:21Z |
| format | Article |
| id | doaj.art-d79f75699ce945b689e09a4ce89958a5 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-11T06:35:21Z |
| publishDate | 2023-03-01 |
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| series | Entropy |
| spelling | doaj.art-d79f75699ce945b689e09a4ce89958a52023-11-17T10:57:24ZengMDPI AGEntropy1099-43002023-03-0125352310.3390/e25030523Remote Sensing Image of The Landsat 8–9 Compressive Sensing via Non-Local Low-Rank Regularization with the Laplace FunctionGuibing Li0Weidong Jin1Jiaqing Miao2Ying Tan3Yingling Li4Weixuan Zhang5Liang Li6School of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, ChinaSchool of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, ChinaSchool of Mathematics, Southwest Minzu University, Chengdu 610041, ChinaKey Laboratory for Computer Systems of State Ethnic Affairs Commission, Southwest Minzu University, Chengdu 610041, ChinaKey Laboratory for Computer Systems of State Ethnic Affairs Commission, Southwest Minzu University, Chengdu 610041, ChinaSchool of Computer Science and Engineering, Southwest Minzu University, Chengdu 610041, ChinaSchool of Electrical Engineering, Southwest Jiaotong University, Chengdu 611756, ChinaUtilizing low-rank prior data in compressed sensing (CS) schemes for Landsat 8–9 remote sensing images (RSIs) has recently received widespread attention. Nevertheless, most CS algorithms focus on the sparsity of an RSI and ignore its low-rank (LR) nature. Therefore, this paper proposes a new CS reconstruction algorithm for Landsat 8–9 remote sensing images based on a non-local optimization framework (NLOF) that is combined with non-convex Laplace functions (NCLF) used for the low-rank approximation (LAA). Since the developed algorithm is based on an approximate low-rank model of the Laplace function, it can adaptively assign different weights to different singular values. Moreover, exploiting the structural sparsity (SS) and low-rank (LR) between the image patches enables the restored image to obtain better CS reconstruction results of Landsat 8–9 RSI than the existing models. For the proposed scheme, first, a CS reconstruction model is proposed using the non-local low-rank regularization (NLLRR) and variational framework. Then, the image patch grouping and Laplace function are used as regularization/penalty terms to constrain the CS reconstruction model. Finally, to effectively solve the rank minimization problem, the alternating direction multiplier method (ADMM) is used to solve the model. Extensive numerical experimental results demonstrate that the non-local variational framework (NLVF) combined with the low-rank approximate regularization (LRAR) method of non-convex Laplace function (NCLF) can obtain better reconstruction results than the more advanced image CS reconstruction algorithms. At the same time, the model preserves the details of Landsat 8–9 RSIs and the boundaries of the transition areas.https://www.mdpi.com/1099-4300/25/3/523compressed sensing (CS)non-local (NL)Laplace function (LF)ADMMLandsat 8–9 remote sensing images (LRSIs) |
| spellingShingle | Guibing Li Weidong Jin Jiaqing Miao Ying Tan Yingling Li Weixuan Zhang Liang Li Remote Sensing Image of The Landsat 8–9 Compressive Sensing via Non-Local Low-Rank Regularization with the Laplace Function Entropy compressed sensing (CS) non-local (NL) Laplace function (LF) ADMM Landsat 8–9 remote sensing images (LRSIs) |
| title | Remote Sensing Image of The Landsat 8–9 Compressive Sensing via Non-Local Low-Rank Regularization with the Laplace Function |
| title_full | Remote Sensing Image of The Landsat 8–9 Compressive Sensing via Non-Local Low-Rank Regularization with the Laplace Function |
| title_fullStr | Remote Sensing Image of The Landsat 8–9 Compressive Sensing via Non-Local Low-Rank Regularization with the Laplace Function |
| title_full_unstemmed | Remote Sensing Image of The Landsat 8–9 Compressive Sensing via Non-Local Low-Rank Regularization with the Laplace Function |
| title_short | Remote Sensing Image of The Landsat 8–9 Compressive Sensing via Non-Local Low-Rank Regularization with the Laplace Function |
| title_sort | remote sensing image of the landsat 8 9 compressive sensing via non local low rank regularization with the laplace function |
| topic | compressed sensing (CS) non-local (NL) Laplace function (LF) ADMM Landsat 8–9 remote sensing images (LRSIs) |
| url | https://www.mdpi.com/1099-4300/25/3/523 |
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