HDSA-Net: Haze Density and Semantic Awareness Network for Hyperspectral Image Dehazing
Hyperspectral image (HSI) dehazing is a challenging task due to the complex imaging conditions. Existing deep learning-based dehazing methods neither fully consider the physical characteristics of HSIs, nor take advantage of high-level semantic information to improve the dehazing performance. To rem...
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| Format: | Article |
| Language: | English |
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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/10820032/ |
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| author | Qianru Liu Tiecheng Song Anyong Qin Yin Liu Feng Yang Chenqiang Gao |
| author_facet | Qianru Liu Tiecheng Song Anyong Qin Yin Liu Feng Yang Chenqiang Gao |
| author_sort | Qianru Liu |
| collection | DOAJ |
| description | Hyperspectral image (HSI) dehazing is a challenging task due to the complex imaging conditions. Existing deep learning-based dehazing methods neither fully consider the physical characteristics of HSIs, nor take advantage of high-level semantic information to improve the dehazing performance. To remedy these, in this article we propose a Haze Density and Semantic Awareness Network (HDSA-Net) for HSI dehazing. Our dual-awareness network not only provides low-level physical information guidance but also high-level semantic guidance for haze removal. Specifically, we estimate the haze density by considering both internal spectral characteristics and external dehazing effects. Based on this, we build a Haze Density Awareness (HDA) block, which enables the network to perceive and focus on difficult dehazing regions with high density. Moreover, we design a Semantic information Extraction Block (SEB) based on the pretrained Segment Anything Model (SAM), followed by several Semantic information Perception Blocks (SPBs), to provide semantic guidance for HSI dehazing. In particular, SEB adapts SAM for the special HSI data and SPBs enable the network to progressively recover semantic information via channel-level coarse guidance and pixel-level fine guidance. The experimental results on simulated and real datasets show the superiority of HDSA-Net over state-of-the-art methods. |
| format | Article |
| id | doaj-art-e9c54fe050184a1a992edeaac065104c |
| 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-e9c54fe050184a1a992edeaac065104c2025-01-30T00:00:13ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01183989400310.1109/JSTARS.2024.352507210820032HDSA-Net: Haze Density and Semantic Awareness Network for Hyperspectral Image DehazingQianru Liu0https://orcid.org/0009-0000-8510-0614Tiecheng Song1https://orcid.org/0000-0003-1264-2812Anyong Qin2https://orcid.org/0000-0002-2538-822XYin Liu3https://orcid.org/0000-0002-4555-1944Feng Yang4https://orcid.org/0000-0003-0413-8640Chenqiang Gao5https://orcid.org/0000-0003-4174-4148School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, ChinaSchool of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, ChinaSchool of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, ChinaSchool of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, ChinaSchool of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, ChinaSchool of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, ChinaHyperspectral image (HSI) dehazing is a challenging task due to the complex imaging conditions. Existing deep learning-based dehazing methods neither fully consider the physical characteristics of HSIs, nor take advantage of high-level semantic information to improve the dehazing performance. To remedy these, in this article we propose a Haze Density and Semantic Awareness Network (HDSA-Net) for HSI dehazing. Our dual-awareness network not only provides low-level physical information guidance but also high-level semantic guidance for haze removal. Specifically, we estimate the haze density by considering both internal spectral characteristics and external dehazing effects. Based on this, we build a Haze Density Awareness (HDA) block, which enables the network to perceive and focus on difficult dehazing regions with high density. Moreover, we design a Semantic information Extraction Block (SEB) based on the pretrained Segment Anything Model (SAM), followed by several Semantic information Perception Blocks (SPBs), to provide semantic guidance for HSI dehazing. In particular, SEB adapts SAM for the special HSI data and SPBs enable the network to progressively recover semantic information via channel-level coarse guidance and pixel-level fine guidance. The experimental results on simulated and real datasets show the superiority of HDSA-Net over state-of-the-art methods.https://ieeexplore.ieee.org/document/10820032/Deep learningdehazinghaze removalhyperspectral imagesSAM |
| spellingShingle | Qianru Liu Tiecheng Song Anyong Qin Yin Liu Feng Yang Chenqiang Gao HDSA-Net: Haze Density and Semantic Awareness Network for Hyperspectral Image Dehazing IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Deep learning dehazing haze removal hyperspectral images SAM |
| title | HDSA-Net: Haze Density and Semantic Awareness Network for Hyperspectral Image Dehazing |
| title_full | HDSA-Net: Haze Density and Semantic Awareness Network for Hyperspectral Image Dehazing |
| title_fullStr | HDSA-Net: Haze Density and Semantic Awareness Network for Hyperspectral Image Dehazing |
| title_full_unstemmed | HDSA-Net: Haze Density and Semantic Awareness Network for Hyperspectral Image Dehazing |
| title_short | HDSA-Net: Haze Density and Semantic Awareness Network for Hyperspectral Image Dehazing |
| title_sort | hdsa net haze density and semantic awareness network for hyperspectral image dehazing |
| topic | Deep learning dehazing haze removal hyperspectral images SAM |
| url | https://ieeexplore.ieee.org/document/10820032/ |
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