Collaboration Between Multiple Experts for Knowledge Adaptation on Multiple Remote Sensing Sources

Ngo, Ba Hung; Kim, Ju Hyun; Park, So Jeong; Cho, Sung In

doi:10.1109/tgrs.2022.3190476

Cited by 7 publications

(3 citation statements)

References 47 publications

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“…To verify the effectiveness of the proposed MS-CADA method, four conventional single-source UDA methods including Li's [57], DAFormer [56], HRDA [58] and PCEL [55], and four multi-source UDA methods including UMMA [41], DCTN [36], He's [32] and MECKA [40], are used for performance comparison.…”

Section: Methods and Measures For Comparisonmentioning

confidence: 99%

“…The problem setting of IDA is to utilize multiple sources with incomplete class for domain adaptation, and the existing few researches mainly focus on the image classification task [36], [37]. Lu et al and Gong et al introduce IDA into the remote sensing field and preliminarily explore the performance of RSIs cross-domain scene classification [38], [39], and Ngo et al further deepen the research on this issue [40]. In addition, Li et al propose to conduct the class-incomplete model adaptation without accessing source information, and design a deep model for street scene semantic segmentation [41].…”

Section: Incomplete and Partial Domain Adaptationmentioning

confidence: 99%

“…Acquiring rich and robust source knowledge is a prerequisite for realizing class asymmetry multi-source domain adaptation. Related researches have shown that simply combining different RSIs into one single source will lead to suboptimal domain adaptation effect due to the domain discrepancies [38]- [40]. Therefore, in each iteration, the RSIs from different sources will be fed into the corresponding expert branches, and supervised training will be conducted on the basis of learning common and source-specific features, which can be expressed as:…”

Section: Multi-source Supervised Learningmentioning

confidence: 99%

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Learning General-Purpose Representations for Cross-Domain Hyperspectral Images Classification with Small Samples

Gao

Xiong

et al. 2023

Remote Sensing

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Cross-domain classification with small samples is a more challenging and realistic experimental setup. Until now, few studies have focused on the problem of small-sample cross-domain classification between completely different hyperspectral images (HSIs) since they possess different land cover types and statistical characteristics. To this end, this paper proposes a general-purpose representation learning method for cross-domain HSI classification, aiming to enable the model to learn more general-purpose deep representations that can quickly adapt to different target domains with small samples. The core of this method is to propose a novel three-level distillation strategy to transfer knowledge from multiple models well-trained on source HSIs into a single distilled model at the channel-, feature- and logit-level simultaneously. The learned representations can be further fine-tuned with small samples and quickly adapt to new target HSIs and previously unseen classes. Specifically, to transfer and fuse knowledge from multiple-source domains into a single model simultaneously and solve the inconsistency of the number of bands in different HSIs, an extensible multi-task model, including the channel transformation module, the feature extraction module and the linear classification module, is designed. Only the feature extraction module is shared across different HSIs, while the other two modules are domain-specific. Furthermore, the typical episode-based learning strategy of the metric-based meta-learning is adopted in the whole learning process to further improve the generalization ability and data efficiency. Extensive experiments are conducted on six source HSIs and four target HSIs, and the results demonstrate that the proposed method outperforms the existing advanced methods in cross-domain HSI classification with small samples.

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