Incorporating inconsistent auxiliary images in haze removal of very high resolution images
Haze contamination is a common issue in very high resolution (VHR) remote sensing images, which inevitably hinders their application. The use of auxiliary images has been a key trend for enhancing haze removal. However, auxiliary VHR images are usually inconsistent with the target hazy VHR images, i...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-05-01
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| Series: | International Journal of Applied Earth Observations and Geoinformation |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1569843223001395 |
| _version_ | 1797828217754615808 |
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| author | Xiaofeng Ma Qunming Wang Xiaohua Tong |
| author_facet | Xiaofeng Ma Qunming Wang Xiaohua Tong |
| author_sort | Xiaofeng Ma |
| collection | DOAJ |
| description | Haze contamination is a common issue in very high resolution (VHR) remote sensing images, which inevitably hinders their application. The use of auxiliary images has been a key trend for enhancing haze removal. However, auxiliary VHR images are usually inconsistent with the target hazy VHR images, in terms of different imaging viewpoints, misalignment, or land cover changes, which greatly influence the performance of haze removal. To handle this problem, this paper develops a novel solution that incorporates auxiliary VHR images without any preprocessing or manual selection for haze removal. Accordingly, a gradient-based haze removal network (GHRN) is constructed. The gradient maps of both the target hazy images and corresponding auxiliary images are fed to the GHRN architecture, and hierarchical convolutions are applied to assist feature alignment. Seven state-of-the-art haze removal methods were employed as comparisons in ten simulated and six real scenarios. The experiment results demonstrate the reliability of GHRN. Moreover, GHRN is suitable for auxiliary images with different contaminations (e.g., haze or noise). To the best of our knowledge, this is the first study to consider inconsistent auxiliary images for enhancing haze removal. |
| first_indexed | 2024-04-09T13:00:54Z |
| format | Article |
| id | doaj.art-d960b19e17f34a4f9c147677912ec66b |
| institution | Directory Open Access Journal |
| issn | 1569-8432 |
| language | English |
| last_indexed | 2024-04-09T13:00:54Z |
| publishDate | 2023-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Applied Earth Observations and Geoinformation |
| spelling | doaj.art-d960b19e17f34a4f9c147677912ec66b2023-05-13T04:24:35ZengElsevierInternational Journal of Applied Earth Observations and Geoinformation1569-84322023-05-01119103317Incorporating inconsistent auxiliary images in haze removal of very high resolution imagesXiaofeng Ma0Qunming Wang1Xiaohua Tong2College of Surveying and Geo-Informatics, Tongji University, 1239 Siping Road, Shanghai 200092, ChinaCorresponding author.; College of Surveying and Geo-Informatics, Tongji University, 1239 Siping Road, Shanghai 200092, ChinaCollege of Surveying and Geo-Informatics, Tongji University, 1239 Siping Road, Shanghai 200092, ChinaHaze contamination is a common issue in very high resolution (VHR) remote sensing images, which inevitably hinders their application. The use of auxiliary images has been a key trend for enhancing haze removal. However, auxiliary VHR images are usually inconsistent with the target hazy VHR images, in terms of different imaging viewpoints, misalignment, or land cover changes, which greatly influence the performance of haze removal. To handle this problem, this paper develops a novel solution that incorporates auxiliary VHR images without any preprocessing or manual selection for haze removal. Accordingly, a gradient-based haze removal network (GHRN) is constructed. The gradient maps of both the target hazy images and corresponding auxiliary images are fed to the GHRN architecture, and hierarchical convolutions are applied to assist feature alignment. Seven state-of-the-art haze removal methods were employed as comparisons in ten simulated and six real scenarios. The experiment results demonstrate the reliability of GHRN. Moreover, GHRN is suitable for auxiliary images with different contaminations (e.g., haze or noise). To the best of our knowledge, this is the first study to consider inconsistent auxiliary images for enhancing haze removal.http://www.sciencedirect.com/science/article/pii/S1569843223001395Haze removalVery high resolution (VHR) imagesDeep learningGradient mapsImage inconsistency |
| spellingShingle | Xiaofeng Ma Qunming Wang Xiaohua Tong Incorporating inconsistent auxiliary images in haze removal of very high resolution images International Journal of Applied Earth Observations and Geoinformation Haze removal Very high resolution (VHR) images Deep learning Gradient maps Image inconsistency |
| title | Incorporating inconsistent auxiliary images in haze removal of very high resolution images |
| title_full | Incorporating inconsistent auxiliary images in haze removal of very high resolution images |
| title_fullStr | Incorporating inconsistent auxiliary images in haze removal of very high resolution images |
| title_full_unstemmed | Incorporating inconsistent auxiliary images in haze removal of very high resolution images |
| title_short | Incorporating inconsistent auxiliary images in haze removal of very high resolution images |
| title_sort | incorporating inconsistent auxiliary images in haze removal of very high resolution images |
| topic | Haze removal Very high resolution (VHR) images Deep learning Gradient maps Image inconsistency |
| url | http://www.sciencedirect.com/science/article/pii/S1569843223001395 |
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