Samba: Semantic Segmentation of Remotely Sensed Images with State Space Model (2404.01705v2)

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 utilizes an encoder-decoder architecture, with Samba blocks serving as the encoder for efficient multi-level semantic information extraction, and UperNet functioning as the decoder. We evaluate Samba on the LoveDA, ISPRS Vaihingen, and ISPRS Potsdam datasets, comparing its performance against top-performing CNN and ViT methods. The results reveal that Samba achieved unparalleled performance on commonly used remote sensing datasets for semantic segmentation. Our proposed Samba demonstrates for the first time the effectiveness of SSM in semantic segmentation of remotely sensed images, setting a new benchmark in performance for Mamba-based techniques in this specific application. The source code and baseline implementations are available at https://github.com/zhuqinfeng1999/Samba.

Semantic Segmentation of Remotely Sensed Images with State Space Models in the Samba Framework

The paper "Samba: Semantic Segmentation of Remotely Sensed Images with State Space Model" introduces an innovative approach to semantic segmentation utilizing a State Space Model (SSM). The primary focus of this research is the development of a semantic segmentation framework named Samba, specifically designed to tackle the challenges of high-resolution remotely sensed images. Existing methods, like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), exhibit limitations when processing such images. CNNs face issues with limited receptive fields, while ViTs grapple with computational complexities due to long sequences and requisite extensive data sets.

The newly proposed Samba framework surpasses these limitations by replacing traditional multi-head self-attention mechanisms in ViTs with a State Space Model, offering more efficient capture of global semantic information without the quadratic computational burdens typically associated with attention mechanisms. Samba utilizes an encoder-decoder architecture, with the novel Samba blocks acting as encoders and employing UperNet as the decoder to perform effective multi-level semantic information extraction. The paper distinguishes itself as it showcases, for the first time, the utilization of SSM in semantic segmentation of high-resolution remote sensing images.

Experimental Validation and Numerical Results

The research includes a rigorous evaluation of the Samba framework against existing top-tier CNN-based and ViT-based methods, implemented without pre-trained parameters. The experiments utilized three widely acknowledged benchmark datasets: LoveDA, ISPRS Vaihingen, and ISPRS Potsdam. Across these datasets, Samba demonstrates superior performance, achieving the highest Mean Intersection over Union (mIoU) scores. Specifically, Samba achieved a 3.95% improvement in mIoU over ViT-based Segformer and a 10.3% improvement over CNN-based ConvNeXt on the LoveDA dataset.

The experimental design highlights Samba's effectiveness in several categorical semantic segmentation tasks, achieving substantial gains in categories such as Building, Water, and Agricultural areas. This performance is attributed to the Samba block's ability to process high-resolution sequences and effectively model global semantics, which is fundamental in segmenting complex remotely sensed imagery.

Architectural and Methodological Insights

The Samba architecture takes foundational inspiration from the effective design of Vision Transformers but incorporates significant enhancements. It replaces the traditional multi-head attention mechanisms with a Mamba block, which employs SSM to capture crucial semantic relationships. The Mamba block within Samba maps input sequences through a latent space, resulting in sequential propagation through linear differential operations.

The computational complexity is managed by discretization operations, which allow the architecture to operate efficiently across lengthy input sequences common in remote sensing tasks. This detailed mathematical modeling ensures that while global semantic information is accurately captured, computational costs remain within manageable limits.

Theoretical and Practical Implications

The findings point towards several implications. Firstly, it highlights the potential of SSM in expanding the capabilities of segmentation frameworks to handle high-resolution and complex image datasets efficiently. Practically, this development can enhance remote sensing applications, like land use classification, urban planning, and environmental monitoring, by providing higher accuracy at lower computational costs.

Future Directions

The paper suggests several future research avenues, emphasizing the exploration of hybrid models that combine the strengths of Mamba with CNN architectures to enhance local feature extraction capabilities. Given the challenge of limited training data in remote sensing, pursuing efficient transfer learning tailored for Mamba is another promising direction. Moreover, exploring applications of Mamba in multi-channel data, such as hyperspectral imagery, could provide valuable insights into complex data sets.

Overall, this paper proposes a significant step forward in the domain of semantic segmentation for remote sensing, laying a robust foundation for future exploration and practical deployments of state-space models in high-resolution image analysis.