Attention Guided Semisupervised Generative Transfer Learning for Hyperspectral Image Analysis
In geospatial image analysis, domain shifts caused by differences between datasets often undermine the performance of deep learning models due to their limited generalization ability. This issue is particularly pronounced in hyperspectral imagery, given the high dimensionality of the per-pixel refle...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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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/10731899/ |
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| author | Anan Yaghmour Saurabh Prasad Melba M. Crawford |
| author_facet | Anan Yaghmour Saurabh Prasad Melba M. Crawford |
| author_sort | Anan Yaghmour |
| collection | DOAJ |
| description | In geospatial image analysis, domain shifts caused by differences between datasets often undermine the performance of deep learning models due to their limited generalization ability. This issue is particularly pronounced in hyperspectral imagery, given the high dimensionality of the per-pixel reflectance vectors and the complexity of the resulting deep learning models. We introduce a semisupervised domain adaptation technique that improves on the adversarial discriminative framework, incorporating a novel multiclass discriminator to address low discriminability and negative transfer issues from which current approaches suffer. Significantly, our method addresses mode collapse by incorporating limited labeled data from the target domain for targeted guidance during adaptation. In addition, we integrate an attention mechanism that focuses on challenging spatial regions for the target mode. We tested our approach on three unique hyperspectral remote sensing datasets to demonstrate its efficacy in diverse conditions (e.g., cloud shadows, atmospheric variability, and terrain). This strategy improves discrimination and reduces negative transfer in domain adaptation for geospatial image analysis. |
| format | Article |
| id | doaj-art-03d06e299d5241e586c8dc2e29e5c47d |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-03d06e299d5241e586c8dc2e29e5c47d2024-11-12T00:00:41ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352024-01-0117198841989910.1109/JSTARS.2024.348552810731899Attention Guided Semisupervised Generative Transfer Learning for Hyperspectral Image AnalysisAnan Yaghmour0https://orcid.org/0009-0007-0048-9757Saurabh Prasad1https://orcid.org/0000-0003-3729-9360Melba M. Crawford2https://orcid.org/0000-0003-3459-2094Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USADepartment of Electrical and Computer Engineering, University of Houston, Houston, TX, USALyles School of Civil Engineering and School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USAIn geospatial image analysis, domain shifts caused by differences between datasets often undermine the performance of deep learning models due to their limited generalization ability. This issue is particularly pronounced in hyperspectral imagery, given the high dimensionality of the per-pixel reflectance vectors and the complexity of the resulting deep learning models. We introduce a semisupervised domain adaptation technique that improves on the adversarial discriminative framework, incorporating a novel multiclass discriminator to address low discriminability and negative transfer issues from which current approaches suffer. Significantly, our method addresses mode collapse by incorporating limited labeled data from the target domain for targeted guidance during adaptation. In addition, we integrate an attention mechanism that focuses on challenging spatial regions for the target mode. We tested our approach on three unique hyperspectral remote sensing datasets to demonstrate its efficacy in diverse conditions (e.g., cloud shadows, atmospheric variability, and terrain). This strategy improves discrimination and reduces negative transfer in domain adaptation for geospatial image analysis.https://ieeexplore.ieee.org/document/10731899/Attention GANsdomain adaptationgenerative adversarial learninghyperspectralremote sensingsemantic segmentation |
| spellingShingle | Anan Yaghmour Saurabh Prasad Melba M. Crawford Attention Guided Semisupervised Generative Transfer Learning for Hyperspectral Image Analysis IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Attention GANs domain adaptation generative adversarial learning hyperspectral remote sensing semantic segmentation |
| title | Attention Guided Semisupervised Generative Transfer Learning for Hyperspectral Image Analysis |
| title_full | Attention Guided Semisupervised Generative Transfer Learning for Hyperspectral Image Analysis |
| title_fullStr | Attention Guided Semisupervised Generative Transfer Learning for Hyperspectral Image Analysis |
| title_full_unstemmed | Attention Guided Semisupervised Generative Transfer Learning for Hyperspectral Image Analysis |
| title_short | Attention Guided Semisupervised Generative Transfer Learning for Hyperspectral Image Analysis |
| title_sort | attention guided semisupervised generative transfer learning for hyperspectral image analysis |
| topic | Attention GANs domain adaptation generative adversarial learning hyperspectral remote sensing semantic segmentation |
| url | https://ieeexplore.ieee.org/document/10731899/ |
| work_keys_str_mv | AT ananyaghmour attentionguidedsemisupervisedgenerativetransferlearningforhyperspectralimageanalysis AT saurabhprasad attentionguidedsemisupervisedgenerativetransferlearningforhyperspectralimageanalysis AT melbamcrawford attentionguidedsemisupervisedgenerativetransferlearningforhyperspectralimageanalysis |