Rethinking Scanning Strategies with Vision Mamba in Semantic Segmentation of Remote Sensing Imagery: An Experimental Study

Understand

Deep learning methods, especially Convolutional Neural Networks (CNN) and Vision Transformer (ViT), are frequently employed to perform semantic segmentation of high-resolution remotely sensed images.

• However, CNNs are constrained by their restricted receptive fields, while ViTs face challenges due to their quadratic complexity.
• Recently, the Mamba model, featuring linear complexity and a global receptive field, has gained extensive attention for vision tasks.
• In such tasks, images need to be serialized to form sequences compatible with the Mamba model.