Semantic segmentation of high-resolution remote sensing images (HRSIs) is a challenging task because objects in HRSIs usually have great scale variance and appearance variance. Although deep convolutional neural networks (DCNNs) have been widely applied in the semantic segmentation of HRSIs, they have inherent limitations in capturing global context. Attention mechanisms and transformer can effectively model long-range dependencies, but they often result in high computational costs when being applied to process HRSIs. In this article, an encoder-decoder network (MSGCNet) is proposed to fully and efficiently model multiscale context and long-range dependencies of HRSIs. Specifically, the multiscale interaction (MSI) module employs an efficient cross-attention to facilitate interaction among multiscale features of the encoder, which bridges the semantic gap between high- and low-level features and introduces more scale information to the network. In order to efficiently model long-range dependencies in both spatial and channel dimensions, the transformer-based decoder block (TBDB) implements window-based efficient multihead self-attention (W-EMSA) and enables interactions cross windows. Furthermore, to further integrate the global context generated by TBDB, the scale-aware fusion (SAF) module is proposed to deeply supervise the decoder, which iteratively fuses hierarchical features through spatial attention. As demonstrated by both quantitative and qualitative experimental results on two publicly available datasets, the proposed MSGCNet exhibits superior performance compared to currently popular methods. The code will be available athttp://github.com/JingxiangZhou/MSGCNet.

Over the past few years, various strategies have been proposed to improve the multi-scale information capture capability of networks, such as encoder-decoder framework, convolution layers with different kernel sizes in parallel and multiple branches framework. However, many methods only rely on one of the strategies, which limits their performance when processing remote sensing images with large scale variance. To address this issue and enable the fast and effective extraction of multi-scale semantic information, this manuscript introduces a novel cross-scale feature propagation network (CFPNet). Specifically, the multi-scale convolution (MSC) module aims to capture fine-grained multi-scale context with different receptive fields, and the attention up-sample (AUS) module embeds the semantic information of high-level features into low-level features while maintaining spatial details. Besides, the feature semantic enhancement (FSE) module is proposed to aggregate the multi-layer features of the decoder to enhance the final feature representation. The experimental results on the BLU and GID datasets demonstrate the effectiveness and efficiency of our CFPNet.