PyramidMamba: Rethinking Pyramid Feature Fusion with Selective Space State Model for Semantic Segmentation of Remote Sensing Imagery
arxiv(2024)
Abstract
Semantic segmentation, as a basic tool for intelligent interpretation of
remote sensing images, plays a vital role in many Earth Observation (EO)
applications. Nowadays, accurate semantic segmentation of remote sensing images
remains a challenge due to the complex spatial-temporal scenes and multi-scale
geo-objects. Driven by the wave of deep learning (DL), CNN- and
Transformer-based semantic segmentation methods have been explored widely, and
these two architectures both revealed the importance of multi-scale feature
representation for strengthening semantic information of geo-objects. However,
the actual multi-scale feature fusion often comes with the semantic redundancy
issue due to homogeneous semantic contents in pyramid features. To handle this
issue, we propose a novel Mamba-based segmentation network, namely
PyramidMamba. Specifically, we design a plug-and-play decoder, which develops a
dense spatial pyramid pooling (DSPP) to encode rich multi-scale semantic
features and a pyramid fusion Mamba (PFM) to reduce semantic redundancy in
multi-scale feature fusion. Comprehensive ablation experiments illustrate the
effectiveness and superiority of the proposed method in enhancing multi-scale
feature representation as well as the great potential for real-time semantic
segmentation. Moreover, our PyramidMamba yields state-of-the-art performance on
three publicly available datasets, i.e. the OpenEarthMap (70.8
Vaihingen (84.8
available at https://github.com/WangLibo1995/GeoSeg.
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