  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. Numerous research efforts have explored
scanning strategies to serialize images, aiming to enhance the Mamba model's
understanding of images. However, the effectiveness of these scanning
strategies remains uncertain. In this research, we conduct a comprehensive
experimental investigation on the impact of mainstream scanning directions and
their combinations on semantic segmentation of remotely sensed images. Through
extensive experiments on the LoveDA, ISPRS Potsdam, and ISPRS Vaihingen
datasets, we demonstrate that no single scanning strategy outperforms others,
regardless of their complexity or the number of scanning directions involved. A
simple, single scanning direction is deemed sufficient for semantic
segmentation of high-resolution remotely sensed images. Relevant directions for
future research are also recommended.
