Feature Transformation for Cross-domain Few-shot Remote Sensing Scene Classification
This addresses the problem of limited training data and domain shifts in remote sensing for researchers and practitioners, though it is incremental as it builds on existing methods with a novel adaptation technique.
The paper tackles the challenge of cross-domain few-shot remote sensing scene classification by proposing a feature-wise transformation module (FTM) that uses a simple affine operation to adapt feature distributions, achieving better performance, transferability, and fine-grained discriminability compared to direct fine-tuning.
Effectively classifying remote sensing scenes is still a challenge due to the increasing spatial resolution of remote imaging and large variances between remote sensing images. Existing research has greatly improved the performance of remote sensing scene classification (RSSC). However, these methods are not applicable to cross-domain few-shot problems where target domain is with very limited training samples available and has a different data distribution from source domain. To improve the model's applicability, we propose the feature-wise transformation module (FTM) in this paper. FTM transfers the feature distribution learned on source domain to that of target domain by a very simple affine operation with negligible additional parameters. Moreover, FTM can be effectively learned on target domain in the case of few training data available and is agnostic to specific network structures. Experiments on RSSC and land-cover mapping tasks verified its capability to handle cross-domain few-shot problems. By comparison with directly finetuning, FTM achieves better performance and possesses better transferability and fine-grained discriminability. \textit{Code will be publicly available.}