Multimodal Remote Sensing Land Cover Data Augmentation and Classification Based on Diffusion Model

Xian Xu, Yinhe Liu, Yinxin Wu, Qi Guo, Kaiyi Xiao, Yanfei Zhong

Abstract: Multimodal remote sensing land cover classification is a significant challenging task. Current methods predominantly rely on deep semantic segmentation models; however, the lack of sufficient representative data and class imbalance among samples obstacles the application of these models on a large-scale area. To overcome these challenges, this paper proposes a semantic segmentation framework based on diffusion model-based data augmentation, leveraging existing classification maps to address data scarcity. The framework comprises four components: pseudo-label generation, multispectral image translation, segmentation model training, and post-processing. Experimental results on the Yangtze River Economic Belt dataset (MMSeg-YREB) from the 2024 IEEE WHISPERS Data Fusion Competition demonstrate the effectiveness of the proposed approach. Additionally, our method achieved second place in the 2024 IEEE WHISPERS competition.

External IDs:dblp:conf/whispers/XuLWGXZ24