CDSS-Pan: Convolutional Dictionary Learning With State Space Model for Pansharpening
Restricted by the development of contemporary sensors, we can only acquire multispectral images (MS) and high-resolution panchromatic (PAN) images separately. The purpose of pansharpening methods is to combine the rich spectral-spatial information contained in MS and PAN images to generate the high-...
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/10772574/ |
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| author | Zixu Li Jintao Song Genji Yuan Jinjiang Li |
| author_facet | Zixu Li Jintao Song Genji Yuan Jinjiang Li |
| author_sort | Zixu Li |
| collection | DOAJ |
| description | Restricted by the development of contemporary sensors, we can only acquire multispectral images (MS) and high-resolution panchromatic (PAN) images separately. The purpose of pansharpening methods is to combine the rich spectral-spatial information contained in MS and PAN images to generate the high-resolution multispectral image. Most existing pansharpening methods either separately extract feature information from MS and PAN images or extract feature information after concatenating MS and PAN images, lacking the utilization of complementary information throughout the feature extraction process. Motivated by the advancements in optimization algorithm and the state space model, we introduce a convolutional dictionary learning with state space model for pansharpeningin this article. Our network comprises two parts: the encoder and the decoder. In the encoder part, we begin by building an observation model to capture the common and unique information between MS and PAN images. Subsequently, we continuously iterate and optimize the network parameters using the approximate gradient algorithm. Meanwhile, we utilize the powerful long-range dependence modeling capability of the SSM to comprehensively extract feature information from the images. In the decoder part, we propose both a detail enhancement block and an adaptive weight learning block to strengthen the model's ability to extract detailed feature information from the images. To demonstrate the superiority of our proposed method, we conduct comparative experiments with current state-of-the-art pansharpening methods on three benchmark datasets: QB, GF2, and WV3. Experimental results prove that our method exhibits the best performance. |
| format | Article |
| id | doaj-art-ecd031529d184a6f8b9342bb6e40a333 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-ecd031529d184a6f8b9342bb6e40a3332025-01-16T00:00:26ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01181734175110.1109/JSTARS.2024.351054510772574CDSS-Pan: Convolutional Dictionary Learning With State Space Model for PansharpeningZixu Li0https://orcid.org/0009-0007-5765-7694Jintao Song1https://orcid.org/0000-0003-0226-0052Genji Yuan2https://orcid.org/0000-0002-8710-2266Jinjiang Li3https://orcid.org/0000-0002-2080-8678School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, ChinaSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, ChinaSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, ChinaSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, ChinaRestricted by the development of contemporary sensors, we can only acquire multispectral images (MS) and high-resolution panchromatic (PAN) images separately. The purpose of pansharpening methods is to combine the rich spectral-spatial information contained in MS and PAN images to generate the high-resolution multispectral image. Most existing pansharpening methods either separately extract feature information from MS and PAN images or extract feature information after concatenating MS and PAN images, lacking the utilization of complementary information throughout the feature extraction process. Motivated by the advancements in optimization algorithm and the state space model, we introduce a convolutional dictionary learning with state space model for pansharpeningin this article. Our network comprises two parts: the encoder and the decoder. In the encoder part, we begin by building an observation model to capture the common and unique information between MS and PAN images. Subsequently, we continuously iterate and optimize the network parameters using the approximate gradient algorithm. Meanwhile, we utilize the powerful long-range dependence modeling capability of the SSM to comprehensively extract feature information from the images. In the decoder part, we propose both a detail enhancement block and an adaptive weight learning block to strengthen the model's ability to extract detailed feature information from the images. To demonstrate the superiority of our proposed method, we conduct comparative experiments with current state-of-the-art pansharpening methods on three benchmark datasets: QB, GF2, and WV3. Experimental results prove that our method exhibits the best performance.https://ieeexplore.ieee.org/document/10772574/Convolutional dictionary learningMambapansharpening |
| spellingShingle | Zixu Li Jintao Song Genji Yuan Jinjiang Li CDSS-Pan: Convolutional Dictionary Learning With State Space Model for Pansharpening IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Convolutional dictionary learning Mamba pansharpening |
| title | CDSS-Pan: Convolutional Dictionary Learning With State Space Model for Pansharpening |
| title_full | CDSS-Pan: Convolutional Dictionary Learning With State Space Model for Pansharpening |
| title_fullStr | CDSS-Pan: Convolutional Dictionary Learning With State Space Model for Pansharpening |
| title_full_unstemmed | CDSS-Pan: Convolutional Dictionary Learning With State Space Model for Pansharpening |
| title_short | CDSS-Pan: Convolutional Dictionary Learning With State Space Model for Pansharpening |
| title_sort | cdss pan convolutional dictionary learning with state space model for pansharpening |
| topic | Convolutional dictionary learning Mamba pansharpening |
| url | https://ieeexplore.ieee.org/document/10772574/ |
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