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 labele...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 per-formance 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 adap-tation 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.展开更多
基金supported by the National Natural Science Foundation of China(62077038,61672405,62176196 and 62271374).
文摘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 per-formance 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 adap-tation 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.
