Data Augmentation and Few-Shot Change Detection in Forest Remote Sensing

Abstract

Forest remote sensing change detection provides an important technical support for forest management decisions and analysis of forest disturbance factors. However, lack of data in specialized fields leads to the detection accuracy improved difficulty. In this study, a forest remote sensing change detection model in the context of few-shot learning is proposed. The proposed model achieves end-to-end change detection algorithm for forest scenes from two perspectives of data augmentation and updated few-shot algorithm. Firstly, forest fragments images are jointly generated by using feature extraction network and generative adversarial network. Then, forest fragments are blended into the original change detection dataset by Poisson blending method to achieve effective augmentation. Furthermore, the end-to-end change detection network is also updated using a few-shot learning. Additionally, Meta-learning module is added to the slow feature analysis algorithm based on the multi-attention mechanism to realize the detection effect improvements. The proposed model improves the F1 score from 86% to 91% on the two datasets. Moreover, it increases the F1 score by 6.52% on average.