PCNEXT: Convolution is All You Need for Semantic Segmentation for Remote Sensing Images

Semantic segmentation of remote sensing images is crucial for various applications, including land use mapping and environmental monitoring. However, most CNNs lack the ability to capture long-range context due to their limited receptive fields. While Transformers adopt multi-head self-attention mechanism to capture long-range context for better accuracy, it often leads to high parameter volume and computational complexity. In this paper, we propose PCNeXt, a lightweight pure-convolutional neural network for semantic segmentation of remote sensing images. For the encoder part, we design a pure-convolutional lightweight module named MSWCA based on MSCA, which is the key component of the encoder of SegNeXt. For the decoder part, we design a purely Convolutional Global-Local Block (CGLB) to replace the GLTB module of UNetFormer, which utilizes the MSWCA module instead of self-attention to capture global context and a simple convolutional operation to capture local context. The experiments prove that instead of using self-attention in transformer, the proposed PCNeXt is capable of achieving competitive accuracy by using only convolutional structure. Notably, our PCNeXt achieves 91.53% aAcc and 83.9 % mIoU on the Potsdam dataset, with only 4.42MB parameters and 6.87G FLOPs calculations.