Few-Shot Learning Remote Scene Classification Based on DC-2DEC

Few-shot learning image classification (FSLIC) is a task that has gained enhanced focus in recent years, the cost of collecting and annotating large number of data samples in some specialised domains is expensive, Few-shot remote scene classification (FRSSC) is of great utility in scenarios where sample is scarce and labelling is extremely costly, the core problem of this task is how to identify new classes with scarce and expensive few-shot samples. However, existing work prefers complicated feature extraction in various ways and the enhancement results are not satisfactory, this paper aims to improve the effectiveness of FSLIC not only through complicated feature extraction but also by exploring alternative approaches. Here are multiple avenues to improve the performance of few-shot classifiers. Training with a scarce data in a few-shot learning (FSL) task often results in a biased feature distribution. In this paper, we propose a method to address this issue by calibrating the support set data feature using sufficient base class data. (Our data distribution calibration method (DC) is on top of feature extractor), requiring no additional parameters. And the feature extraction model is further optimised and the feature extractor of DC-2DEC is optimised with the task of dealing with the spatial context structure of the image i.e. rotation prediction pretext, specifically rotation prediction. We refer to the proposed method as DC-2DEC, and we apply it to few-shot learning classification in RS image (RS image) scene recognition. Through experiments conducted on traditional few-shot datasets and RS image datasets, we validate the algorithm and present corresponding experimental results. These results demonstrate the competitiveness of DC-2DEC, highlighting its efficacy in few-shot learning classification for RS images.