Collaborative Self-Supervised Transductive Few-Shot Learning for Remote Sensing Scene Classification

Abstract

With the advent of deep learning and the accessibility of massive data, scene classification algorithms based on deep learning have been extensively researched and have achieved exciting developments. However, the success of deep models often relies on a large amount of annotated remote sensing data. Additionally, deep models are typically trained and tested on the same set of classes, leading to compromised generalization performance when encountering new classes. This is where few-shot learning aims to enable models to quickly generalize to new classes with only a few reference samples. In this paper, we propose a novel collaborative self-supervised transductive few-shot learning (CS2TFSL) algorithm for remote sensing scene classification. In our approach, we construct two distinct self-supervised auxiliary tasks to jointly train the feature extractor, aiming to obtain a powerful representation. Subsequently, the feature extractor’s parameters are frozen, requiring no further training, and transferred to the inference stage. During testing, we employ transductive inference to enhance the associative information between the support and query sets by leveraging additional sample information in the data. Extensive comparisons with state-of-the-art few-shot scene classification algorithms on the WHU-RS19 and NWPU-RESISC45 datasets demonstrate the effectiveness of the proposed CS2TFSL. More specifically, CS2TFSL ranks first in the settings of five-way one-shot and five-way five-shot. Additionally, detailed ablation experiments are conducted to analyze the CS2TFSL. The experimental results reveal significant and promising performance improvements in few-shot scene classification through the combination of self-supervised learning and direct transductive inference.