Less Is More: A Lightweight Deep Learning Network for Remote Sensing Imagery Segmentation

High resolution remote sensing imageries contain a wealth of information about ground object features. Semantic segmentation of high resolution remote sensing imageries has emerged as a prominent area of research. However, many existing models have become complex in pursuit of accuracy, making it challenging to achieve a balance between efficiency and precision. To address this issue, we propose LiteSeger, a lightweight semantic segmentation model that combines the strengths of convolutional neural network (CNN) and transformer architectures. Initially, the MobileViT backbone network, which integrates CNN and Transformer, is employed to rapidly extract multi-level features. Subsequently, we devised a Global Attention mechanism and a Local Attention mechanism, respectively, for low-level and high-level features, which we incorporated into the decoder as the Main Feature Fusion (MFF) module. Local Attention enhances the representation of detailed features in both spatial and channel dimensions. Global Attention strengthens global interconnectivity through long-range semantic information. Particularly, Our proposed Distance-aware Grouped Attention demonstrates a significant reduction in the computational complexity of attention computation, without compromising segmentation accuracy. In comparison to other light-weight models, our proposed LiteSeger achieves the best MIoU and Ave.F1 on both the Vaihingen and Potsdam datasets.On the Vaihingen dataset, it achieves an MIoU of 71.22 and an Ave.F1 of 82.97. On the Potsdam dataset, the MIoU and Ave.F1 reach 72.00 and 83.34, respectively. At the same time, it has significant advantages in inference efficiency. LiteSeger contains only 7.0 M parameters and under the experimental settings of this study, it achieves an inference speed of 50 FPS. The source codes are available at https://github.com/HIGISX/LiteSeger.