Reproduce “PyramidMamba: Rethinking Pyramid Feature Fusion with Selective Space State Model for Semantic Segmentation of Remote Sensing Imagery” on OpenResearch

About this paper

Libo Wang, Dongxu Li, Sijun Dong, Xiaoliang Meng, Xiaokang Zhang, Danfeng Hong

PyramidMamba is introduced as a Mamba-based network for semantic segmentation of remote sensing imagery, integrating a Dense Spatial Pyramid Pooling (DSPP) module and a Pyramid Fusion Mamba (PFM) module to enhance multi-scale feature representation. The network selectively reduces semantic redundancy, achieving competitive accuracy and efficiency across OpenEarthMap, ISPRS Vaihingen, and ISPRS Potsdam datasets, including 83.1% mIoU at 73.8 FPS on Vaihingen for real-time applications.

Key insights

• The Mamba architecture's selective scan mechanism effectively compresses homogeneous features and extracts core semantic information, offering an efficient alternative to self-attention for processing long sequences.
• Employing dense spatial pyramid pooling significantly enriches multi-scale contextual information, which is crucial for achieving fine-grained and accurate semantic segmentation in remote sensing images.
• Integrating Mamba's selective filtering within the pyramid feature fusion process can effectively alleviate semantic redundancy, leading to improved multi-scale visual representation and overall segmentation performance.

Summary by alphaXiv.