A class distribution learning method for few-shot remote sensing scene classification

ABSTRACT

In recent years, few-shot remote sensing scene classification has become an important foundational research topic, aiming to achieve better classification performance with limited labeled data. Due to the large intra-class variances and inter-class similarity of remote sensing scenes, the scene classification task for remote sensing is much more challenging than general few-shot image classification. In this letter, we propose a class distribution learning method under limited samples by fully using the support samples, which consists of intra-class feature aggregation (IFA) module, intra-class feature homogenization module (IFH) and inter-class cross calibration (ICC) module. IFA and IFH modules are proposed to enable the aggregation and even distribution of intra-class features around the prototypes generated by the model, reducing intra-class distances and mitigating the generation of extreme samples near decision boundaries. ICC module is designed to encourage the features generated by the model for inter-class to be further apart, thereby increasing the inter-class distance and reducing the probability of classification errors. To demonstrate the effectiveness of our proposed method, we conduct comprehensive experiments on three benchmark remote sensing scene datasets: NWPU-RESISC45, WHU-RS19, and UC Merced, achieving extremely advanced performance. Our source codes have been available at https://github.com/mitsuha97/CDLM.

KEYWORDS:

• Few-shot learning
• remote sensing image scene classification
• prototypical network

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [62271302].