A Novel Adaptively Optimized PCNN Model for Hyperspectral Image Sharpening
Hyperspectral satellite imagery has developed rapidly over the last decade because of its high spectral resolution and strong material recognition capability. Nonetheless, the spatial resolution of available hyperspectral imagery is inferior, severely affecting the accuracy of ground object identifi...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/17/4205 |
| _version_ | 1797581932425379840 |
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| author | Xinyu Xu Xiaojun Li Yikun Li Lu Kang Junfei Ge |
| author_facet | Xinyu Xu Xiaojun Li Yikun Li Lu Kang Junfei Ge |
| author_sort | Xinyu Xu |
| collection | DOAJ |
| description | Hyperspectral satellite imagery has developed rapidly over the last decade because of its high spectral resolution and strong material recognition capability. Nonetheless, the spatial resolution of available hyperspectral imagery is inferior, severely affecting the accuracy of ground object identification. In the paper, we propose an adaptively optimized pulse-coupled neural network (PCNN) model to sharpen the spatial resolution of the hyperspectral imagery to the scale of the multispectral imagery. Firstly, a SAM-CC strategy is designed to assign hyperspectral bands to the multispectral bands. Subsequently, an improved PCNN (IPCNN) is proposed, which considers the differences of the neighboring neurons. Furthermore, the Chameleon Swarm Optimization (CSA) optimization is adopted to generate the optimum fusion parameters for IPCNN. Hence, the injected spatial details are acquired in the irregular regions generated by the IPCNN. Extensive experiments are carried out to validate the superiority of the proposed model, which confirms that our method can realize hyperspectral imagery with high spatial resolution, yielding the best spatial details and spectral information among the state-of-the-art approaches. Several ablation studies further corroborate the efficiency of our method. |
| first_indexed | 2024-03-10T23:14:39Z |
| format | Article |
| id | doaj.art-b34e10f97be843e49028e2e2f7189f06 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T23:14:39Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b34e10f97be843e49028e2e2f7189f062023-11-19T08:45:55ZengMDPI AGRemote Sensing2072-42922023-08-011517420510.3390/rs15174205A Novel Adaptively Optimized PCNN Model for Hyperspectral Image SharpeningXinyu Xu0Xiaojun Li1Yikun Li2Lu Kang3Junfei Ge4Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, ChinaState Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, CAS, Beijing 100101, ChinaFaculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, ChinaHyperspectral satellite imagery has developed rapidly over the last decade because of its high spectral resolution and strong material recognition capability. Nonetheless, the spatial resolution of available hyperspectral imagery is inferior, severely affecting the accuracy of ground object identification. In the paper, we propose an adaptively optimized pulse-coupled neural network (PCNN) model to sharpen the spatial resolution of the hyperspectral imagery to the scale of the multispectral imagery. Firstly, a SAM-CC strategy is designed to assign hyperspectral bands to the multispectral bands. Subsequently, an improved PCNN (IPCNN) is proposed, which considers the differences of the neighboring neurons. Furthermore, the Chameleon Swarm Optimization (CSA) optimization is adopted to generate the optimum fusion parameters for IPCNN. Hence, the injected spatial details are acquired in the irregular regions generated by the IPCNN. Extensive experiments are carried out to validate the superiority of the proposed model, which confirms that our method can realize hyperspectral imagery with high spatial resolution, yielding the best spatial details and spectral information among the state-of-the-art approaches. Several ablation studies further corroborate the efficiency of our method.https://www.mdpi.com/2072-4292/15/17/4205hyperspectral sharpeningpulse-coupled neural networkmultispectral imageremote sensing image fusionhigh-resolution image |
| spellingShingle | Xinyu Xu Xiaojun Li Yikun Li Lu Kang Junfei Ge A Novel Adaptively Optimized PCNN Model for Hyperspectral Image Sharpening Remote Sensing hyperspectral sharpening pulse-coupled neural network multispectral image remote sensing image fusion high-resolution image |
| title | A Novel Adaptively Optimized PCNN Model for Hyperspectral Image Sharpening |
| title_full | A Novel Adaptively Optimized PCNN Model for Hyperspectral Image Sharpening |
| title_fullStr | A Novel Adaptively Optimized PCNN Model for Hyperspectral Image Sharpening |
| title_full_unstemmed | A Novel Adaptively Optimized PCNN Model for Hyperspectral Image Sharpening |
| title_short | A Novel Adaptively Optimized PCNN Model for Hyperspectral Image Sharpening |
| title_sort | novel adaptively optimized pcnn model for hyperspectral image sharpening |
| topic | hyperspectral sharpening pulse-coupled neural network multispectral image remote sensing image fusion high-resolution image |
| url | https://www.mdpi.com/2072-4292/15/17/4205 |
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