IDLN: Iterative Distribution Learning Network for Few-Shot Remote Sensing Image Scene Classification
Qingjie Zeng, Jie Geng, Wen Jiang, Kai Huang, Zeyu Wang
Abstract
Few-shot remote sensing image scene classification has gained more attention due to its ability to recognize novel categories with several annotated samples. However, it is a great challenge to extract category characteristics with insufficient labeled samples. To address this issue, an iterative distribution learning network (IDLN) is proposed for the scene classification of few-shot remote sensing images. Specifically, the proposed model is a cyclic iterative architecture, which is composed of three modules to enhance the classification performance. In each iteration, similarity distribution learning module is proposed to calculate feature relations among instances first, then label mapping module is developed as the few-shot classifier based on instructive knowledge, and, finally, the attention-based feature calibration module is proposed to modify features based on label relations, and the calibrated features are imported to the next iteration. Experimental results on two public remote sensing datasets demonstrate that the proposed network is able to achieve superior performance for few-shot remote sensing image scene classification.