Unsupervised meta-learning with domain adaptation based on a multi-task reconstruction-classification network for few-shot hyperspectral image classification
Although the deep-learning method has achieved great success for hyperspectral image (HSI) classification, the few-shot HSI classification deserves sufficient study because it is difficult and expensive to acquire labeled samples. In fact, the meta-learning methods can improve the performance for fe...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-03-01
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| Series: | Journal of Information and Intelligence |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949715924000544 |
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| author | Yu Liu Caihong Mu Shanjiao Jiang Yi Liu |
| author_facet | Yu Liu Caihong Mu Shanjiao Jiang Yi Liu |
| author_sort | Yu Liu |
| collection | DOAJ |
| description | Although the deep-learning method has achieved great success for hyperspectral image (HSI) classification, the few-shot HSI classification deserves sufficient study because it is difficult and expensive to acquire labeled samples. In fact, the meta-learning methods can improve the performance for few-shot HSI classification effectively. However, most of the existing meta-learning methods for HSI classification are supervised, which still heavily rely on the labeled data for meta-training. Moreover, there are many cross-scene classification tasks in the real world, and domain adaptation of unsupervised meta-learning has been ignored for HSI classification so far. To address the above issues, this paper proposes an unsupervised meta-learning method with domain adaptation based on a multi-task reconstruction-classification network (MRCN) for few-shot HSI classification. MRCN does not need any labeled data for meta-training, where the pseudo labels are generated by multiple spectral random sampling and data augmentation. The meta-training of MRCN jointly learns a shared encoding representation for two tasks and domains. On the one hand, we design an encoder-classifier to learn the classification task on the source-domain data. On the other hand, we devise an encoder-decoder to learn the reconstruction task on the target-domain data. The experimental results on four HSI datasets demonstrate that MRCN preforms better than several state-of-the-art methods with only two to five labeled samples per class. To the best of our knowledge, the proposed method is the first unsupervised meta-learning method that considers the domain adaptation for few-shot HSI classification. |
| format | Article |
| id | doaj-art-1c83763f0fca43eab6dbc3ee1f494833 |
| institution | Kabale University |
| issn | 2949-7159 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Information and Intelligence |
| spelling | doaj-art-1c83763f0fca43eab6dbc3ee1f4948332025-08-20T03:32:53ZengKeAi Communications Co., Ltd.Journal of Information and Intelligence2949-71592025-03-013210311210.1016/j.jiixd.2024.06.001Unsupervised meta-learning with domain adaptation based on a multi-task reconstruction-classification network for few-shot hyperspectral image classificationYu Liu0Caihong Mu1Shanjiao Jiang2Yi Liu3Guangzhou Institute of Technology, Xidian University, Guangzhou 510555, ChinaSchool of Artificial Intelligence, Xidian University, Xi'an 710071, ChinaGuangzhou Institute of Technology, Xidian University, Guangzhou 510555, ChinaSchool of Electronic Engineering, Xidian University, Xi'an 710071, China; Corresponding author.Although the deep-learning method has achieved great success for hyperspectral image (HSI) classification, the few-shot HSI classification deserves sufficient study because it is difficult and expensive to acquire labeled samples. In fact, the meta-learning methods can improve the performance for few-shot HSI classification effectively. However, most of the existing meta-learning methods for HSI classification are supervised, which still heavily rely on the labeled data for meta-training. Moreover, there are many cross-scene classification tasks in the real world, and domain adaptation of unsupervised meta-learning has been ignored for HSI classification so far. To address the above issues, this paper proposes an unsupervised meta-learning method with domain adaptation based on a multi-task reconstruction-classification network (MRCN) for few-shot HSI classification. MRCN does not need any labeled data for meta-training, where the pseudo labels are generated by multiple spectral random sampling and data augmentation. The meta-training of MRCN jointly learns a shared encoding representation for two tasks and domains. On the one hand, we design an encoder-classifier to learn the classification task on the source-domain data. On the other hand, we devise an encoder-decoder to learn the reconstruction task on the target-domain data. The experimental results on four HSI datasets demonstrate that MRCN preforms better than several state-of-the-art methods with only two to five labeled samples per class. To the best of our knowledge, the proposed method is the first unsupervised meta-learning method that considers the domain adaptation for few-shot HSI classification.http://www.sciencedirect.com/science/article/pii/S2949715924000544Unsupervised meta-learningDomain adaptationMulti-task learningReconstruction-classification network |
| spellingShingle | Yu Liu Caihong Mu Shanjiao Jiang Yi Liu Unsupervised meta-learning with domain adaptation based on a multi-task reconstruction-classification network for few-shot hyperspectral image classification Journal of Information and Intelligence Unsupervised meta-learning Domain adaptation Multi-task learning Reconstruction-classification network |
| title | Unsupervised meta-learning with domain adaptation based on a multi-task reconstruction-classification network for few-shot hyperspectral image classification |
| title_full | Unsupervised meta-learning with domain adaptation based on a multi-task reconstruction-classification network for few-shot hyperspectral image classification |
| title_fullStr | Unsupervised meta-learning with domain adaptation based on a multi-task reconstruction-classification network for few-shot hyperspectral image classification |
| title_full_unstemmed | Unsupervised meta-learning with domain adaptation based on a multi-task reconstruction-classification network for few-shot hyperspectral image classification |
| title_short | Unsupervised meta-learning with domain adaptation based on a multi-task reconstruction-classification network for few-shot hyperspectral image classification |
| title_sort | unsupervised meta learning with domain adaptation based on a multi task reconstruction classification network for few shot hyperspectral image classification |
| topic | Unsupervised meta-learning Domain adaptation Multi-task learning Reconstruction-classification network |
| url | http://www.sciencedirect.com/science/article/pii/S2949715924000544 |
| work_keys_str_mv | AT yuliu unsupervisedmetalearningwithdomainadaptationbasedonamultitaskreconstructionclassificationnetworkforfewshothyperspectralimageclassification AT caihongmu unsupervisedmetalearningwithdomainadaptationbasedonamultitaskreconstructionclassificationnetworkforfewshothyperspectralimageclassification AT shanjiaojiang unsupervisedmetalearningwithdomainadaptationbasedonamultitaskreconstructionclassificationnetworkforfewshothyperspectralimageclassification AT yiliu unsupervisedmetalearningwithdomainadaptationbasedonamultitaskreconstructionclassificationnetworkforfewshothyperspectralimageclassification |