Paper Info
Title
Spatial-Spectral Local Domain Adaption for Cross Domain Few Shot Hyperspectral Images Classification
Abstract
The traditional methods of hyperspectral image (HSI) classification are based on the sufficient labeled data. In real life, we often encounter that the target domain corresponding to the classification task has only a small amount of labeled data, but the source domain has enough labeled data. However, the distribution of the source domain is different from the distribution of the target domain. Thus, the labeled data of the source domain cannot be applied to the target domain directly. This article proposes a new method to solve the cross-domain few shot problem of HSI classification. In the proposed method, the local spatial alignment and the spectral alignment are simultaneously introduced to transfer the knowledge from the source domain to the target domain. Besides, to extract the domain-specific features, we balance the domain-invariant features and the domain-specific features by a weakly parameter-shared mechanism. The two modules together can narrow the distance between two domains and make the model perform well on the target domain. Experiments conducted on four different target domain datasets demonstrate the effectiveness of our method.
Year
DOI
Venue
2022
10.1109/TGRS.2022.3208897
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Keywords
DocType
Volume
Feature extraction, Deep learning, Training, Adaptation models, Transfer learning, Three-dimensional displays, Principal component analysis, Cross domain few shot learning (CDFSL), hyperspectral image (HSI), local alignment, weakly parameter-shared mechanism
Journal
60
ISSN
Citations 
PageRank 
0196-2892
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Biqi Wang100.34
Yang Xu271183.57
Zebin Wu326030.82
Tianming Zhan400.68
Zhihui Wei542850.68