Clusterformer for Pine Tree Disease Identification Based on UAV Remote Sensing Image Segmentation

Pine wilt disease (PWD) is one of the most prevalent pine trees diseases, resulting in both ecological and economic havoc. UAV remote sensing segmentation plays a crucial role in early identifying and preventing PWD. However, deep learning segmentation models customized for PWD identification in scenarios with complex backgrounds have not received extensive exploration. In this paper, we propose a novel UAV remote sensing segmentation model called Clusterformer with a conventional encoder-decoder structure. The encoder is comprised of the specially designed Cluster Transformer, which includes a cluster token mixer and a spatial-channel feed-forward network (SC-FFN). The cluster token mixer utilizes constructed clusters from the feature maps to represent pixels, thereby reducing redundant and interfering information. The SC-FFN extracts multi-scale spatial information through depth-wise convolutions and channel information through a multilayer perceptron in sequence. The decoder primarily consists of the specially designed D-Cluster Transformer. The token mixer of the D-Cluster Transformer employs constructed clusters from high-level decoded tokens to represent low-level encoded tokens without relying on traditional upsampling methods such as interpolation, transpose convolution, or patch expansion. Consequently, more robust and less redundant features from high-level decoded feature maps are transferred to low-level encoded feature maps. Experimental results on two PWD datasets demonstrate that Clusterformer outperforms existing state-of-the-art segmentation models. This confirms the effectiveness and efficiency of Clusterformer in PWD identification. Code is available at https://github.com/huanliu233/Clusterformer.