Few-Shot Unsupervised Domain Adaptation Based on Refined Bi-Directional Prototypical Contrastive Learning for Cross-Scene Hyperspectral Image Classification
Hyperspectral image cross-scene classification (HSICC) tasks are confronted with tremendous challenges due to spectral shift phenomena across scenes and the tough work of obtaining labels. Unsupervised domain adaptation has proven its effectiveness in tackling this issue, but it has a fatal limitati...
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MDPI AG
2025-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/13/2305 |
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| author | Xuebin Tang Hanyi Shi Chunchao Li Cheng Jiang Xiaoxiong Zhang Lingbin Zeng Xiaolei Zhou |
| author_facet | Xuebin Tang Hanyi Shi Chunchao Li Cheng Jiang Xiaoxiong Zhang Lingbin Zeng Xiaolei Zhou |
| author_sort | Xuebin Tang |
| collection | DOAJ |
| description | Hyperspectral image cross-scene classification (HSICC) tasks are confronted with tremendous challenges due to spectral shift phenomena across scenes and the tough work of obtaining labels. Unsupervised domain adaptation has proven its effectiveness in tackling this issue, but it has a fatal limitation of intending to narrow the disparity between source and target domains by utilizing fully labeled source data and unlabeled target data. However, it is costly even to attain labels from source domains in many cases, rendering sufficient labeling as used in prior work impractical. In this work, we investigate an extreme and realistic scenario where unsupervised domain adaptation methods encounter sparsely labeled source data when handling HSICC tasks, namely, few-shot unsupervised domain adaptation. We propose an end-to-end refined bi-directional prototypical contrastive learning (RBPCL) framework for overcoming the HSICC problem with only a few labeled samples in the source domain. RBPCL captures category-level semantic features of hyperspectral data and performs feature alignment through in-domain refined prototypical self-supervised learning and bi-directional cross-domain prototypical contrastive learning, respectively. Furthermore, our framework introduces the class-balanced multicentric dynamic prototype strategy to generate more robust and representative prototypes. To facilitate prototype contrastive learning, we employ a Siamese-style distance metric loss function to aggregate intra-class features while increasing the discrepancy of inter-class features. Finally, extensive experiments and ablation analysis implemented on two public cross-scene data pairs and three pairs of self-collected ultralow-altitude hyperspectral datasets under different illumination conditions verify the effectiveness of our method, which will further enhance the practicality of hyperspectral intelligent sensing technology. |
| format | Article |
| id | doaj-art-297a70106cf04807b6049a9cbc297471 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-297a70106cf04807b6049a9cbc2974712025-08-20T03:28:59ZengMDPI AGRemote Sensing2072-42922025-07-011713230510.3390/rs17132305Few-Shot Unsupervised Domain Adaptation Based on Refined Bi-Directional Prototypical Contrastive Learning for Cross-Scene Hyperspectral Image ClassificationXuebin Tang0Hanyi Shi1Chunchao Li2Cheng Jiang3Xiaoxiong Zhang4Lingbin Zeng5Xiaolei Zhou6National University of Defense Technology, Changsha 410073, ChinaNational University of Defense Technology, Changsha 410073, ChinaNational University of Defense Technology, Changsha 410073, ChinaNational University of Defense Technology, Changsha 410073, ChinaNational University of Defense Technology, Changsha 410073, ChinaNational University of Defense Technology, Changsha 410073, ChinaNational University of Defense Technology, Changsha 410073, ChinaHyperspectral image cross-scene classification (HSICC) tasks are confronted with tremendous challenges due to spectral shift phenomena across scenes and the tough work of obtaining labels. Unsupervised domain adaptation has proven its effectiveness in tackling this issue, but it has a fatal limitation of intending to narrow the disparity between source and target domains by utilizing fully labeled source data and unlabeled target data. However, it is costly even to attain labels from source domains in many cases, rendering sufficient labeling as used in prior work impractical. In this work, we investigate an extreme and realistic scenario where unsupervised domain adaptation methods encounter sparsely labeled source data when handling HSICC tasks, namely, few-shot unsupervised domain adaptation. We propose an end-to-end refined bi-directional prototypical contrastive learning (RBPCL) framework for overcoming the HSICC problem with only a few labeled samples in the source domain. RBPCL captures category-level semantic features of hyperspectral data and performs feature alignment through in-domain refined prototypical self-supervised learning and bi-directional cross-domain prototypical contrastive learning, respectively. Furthermore, our framework introduces the class-balanced multicentric dynamic prototype strategy to generate more robust and representative prototypes. To facilitate prototype contrastive learning, we employ a Siamese-style distance metric loss function to aggregate intra-class features while increasing the discrepancy of inter-class features. Finally, extensive experiments and ablation analysis implemented on two public cross-scene data pairs and three pairs of self-collected ultralow-altitude hyperspectral datasets under different illumination conditions verify the effectiveness of our method, which will further enhance the practicality of hyperspectral intelligent sensing technology.https://www.mdpi.com/2072-4292/17/13/2305remote sensinghyperspectral imaging cross-scene classification (HSICC)few-shot unsupervised domain adaptation (FUDA)refined bi-directional prototypical contrastive learning (RBPCL)self-collected ultralow-altitude hyperspectral datasets |
| spellingShingle | Xuebin Tang Hanyi Shi Chunchao Li Cheng Jiang Xiaoxiong Zhang Lingbin Zeng Xiaolei Zhou Few-Shot Unsupervised Domain Adaptation Based on Refined Bi-Directional Prototypical Contrastive Learning for Cross-Scene Hyperspectral Image Classification Remote Sensing remote sensing hyperspectral imaging cross-scene classification (HSICC) few-shot unsupervised domain adaptation (FUDA) refined bi-directional prototypical contrastive learning (RBPCL) self-collected ultralow-altitude hyperspectral datasets |
| title | Few-Shot Unsupervised Domain Adaptation Based on Refined Bi-Directional Prototypical Contrastive Learning for Cross-Scene Hyperspectral Image Classification |
| title_full | Few-Shot Unsupervised Domain Adaptation Based on Refined Bi-Directional Prototypical Contrastive Learning for Cross-Scene Hyperspectral Image Classification |
| title_fullStr | Few-Shot Unsupervised Domain Adaptation Based on Refined Bi-Directional Prototypical Contrastive Learning for Cross-Scene Hyperspectral Image Classification |
| title_full_unstemmed | Few-Shot Unsupervised Domain Adaptation Based on Refined Bi-Directional Prototypical Contrastive Learning for Cross-Scene Hyperspectral Image Classification |
| title_short | Few-Shot Unsupervised Domain Adaptation Based on Refined Bi-Directional Prototypical Contrastive Learning for Cross-Scene Hyperspectral Image Classification |
| title_sort | few shot unsupervised domain adaptation based on refined bi directional prototypical contrastive learning for cross scene hyperspectral image classification |
| topic | remote sensing hyperspectral imaging cross-scene classification (HSICC) few-shot unsupervised domain adaptation (FUDA) refined bi-directional prototypical contrastive learning (RBPCL) self-collected ultralow-altitude hyperspectral datasets |
| url | https://www.mdpi.com/2072-4292/17/13/2305 |
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