Samba: Semantic Segmentation of Remotely Sensed Images with State Space Model

arXiv:2404.01705v2 Announce Type: replace
Abstract: High-resolution remotely sensed images pose a challenge for commonly used semantic segmentation methods such as Convolutional Neural Network (CNN) and Vision Transformer (ViT). CNN-based methods struggle with handling such high-resolution images due to their limited receptive field, while ViT faces challenges in handling long sequences. Inspired by Mamba, which adopts a State Space Model (SSM) to efficiently capture global semantic information, we propose a semantic segmentation framework for high-resolution remotely sensed images, named Samba. Samba …

arxiv cs.cv images segmentation semantic space state state space model type