Wetland Segmentation Method for UAV Multispectral Remote Sensing Images Based on SegFormer

In this study, an end-to-end semantic segmentation method (ConvSegFormer) is proposed by utilizing the multispectral imaging capability of UAVs for images containing multispectral bands, with a special focus on thermal infrared bands. Experimental results show that the use of multispectral images, especially thermal infrared bands, achieves higher segmentation accuracy through spectral information. In addition, the end-to-end deep learning semantic segmentation method can directly learn the complex mapping relationship between image pixels and semantic categories without step-by-step feature extraction and classification, which is more direct and efficient. Finally, the maximum values of Mean Pixel Accuracy (MPA) and Mean Intersection Over Union (MIOU) are 90.35% and 73.87%. In the segmentation task of the wetland area, the maximum values of PA and IOU reached 95.42% and 90.46%. This indicates that the method is effective and feasible in automatically extracting the segmentation of wetlands and other land types.