Cross-domain few-shot classification for remote sensing imagery

Abstract

Deep learning has proven highly effective for scene classification tasks when substantial quantities of labelled data are accessible. However, performance decreases when applied to domains such as remote sensing which typically possess a limited quantity of labelled data across available datasets. Few-shot learning has been developed as one of the promising solutions to this problem. It has the ability to recognise new categories with minimal labelled examples, but it assumes that the training and testing data will exhibit identical feature distributions. This assumption is unrealistic in real-world contexts where data can originate from different domains and poses a challenge when a significant domain shift exists between the training and testing data. This dissertation aims to address these limitations by proposing the Cross-Domain Attention Network (CDAN). It is a network designed specifically to solve the issues that arise when there is a limited quantity of labelled data available and a significant domain shift exists between the training and testing data. The network proposed consists of a prototypical network as the base and three additions that contribute to the accurate scene classification of remote sensing imagery. Firstly, a cross-domain data augmentation technique is proposed with few-shot learning to reduce domain shift. The cross-domain data augmentation technique facilitates enhanced knowledge transfer between domains and increases the adaptation ability of the network, whereas few-shot learning reduces the network’s reliance on large labelled datasets. Secondly, a dynamic and focused attention module is proposed to improve discriminative capacity of the network by increasing the focus on important channels and spatial regions within images during training. Thirdly, an adaptive task aware loss is proposed to further enhance the network’s adaptive capacity by leveraging information in few-shot training tasks. Extensive experiments are carried out with different remote imaging classification datasets (RSICB, AID and NWPU-RESISC45) to prove that the proposed network alleviates concerns in a cross-domain few-shot (CDFS) classification setting.