Rethinking Scanning Strategies with Vision Mamba in Semantic Segmentation of Remote Sensing Imagery: An Experimental Study

• URL: http://arxiv.org/abs/2405.08493v1
• Date: Tue, 14 May 2024 10:36:56 GMT
• Title: Rethinking Scanning Strategies with Vision Mamba in Semantic Segmentation of Remote Sensing Imagery: An Experimental Study
• Authors: Qinfeng Zhu, Yuan Fang, Yuanzhi Cai, Cheng Chen, Lei Fan,
• Abstract summary: We investigate the impact of mainstream scanning directions and their combinations on semantic segmentation of images. A simple, single scanning direction is deemed sufficient for semantic segmentation of high-resolution remotely sensed images.
• Score: 7.334290421966221
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• DAMamba: Vision State Space Model with Dynamic Adaptive Scan [51.81060691414399]
  State space models (SSMs) have recently garnered significant attention in computer vision. We propose Dynamic Adaptive Scan (DAS), a data-driven method that adaptively allocates scanning orders and regions. Based on DAS, we propose the vision backbone DAMamba, which significantly outperforms current state-of-the-art vision Mamba models in vision tasks.
  arXiv  Detail & Related papers  (2025-02-18T08:12:47Z)
• Semi-supervised Semantic Segmentation for Remote Sensing Images via Multi-scale Uncertainty Consistency and Cross-Teacher-Student Attention [59.19580789952102]
  This paper proposes a novel semi-supervised Multi-Scale Uncertainty and Cross-Teacher-Student Attention (MUCA) model for RS image semantic segmentation tasks. MUCA constrains the consistency among feature maps at different layers of the network by introducing a multi-scale uncertainty consistency regularization. MUCA utilizes a Cross-Teacher-Student attention mechanism to guide the student network, guiding the student network to construct more discriminative feature representations.
  arXiv  Detail & Related papers  (2025-01-18T11:57:20Z)
• MambaCSR: Dual-Interleaved Scanning for Compressed Image Super-Resolution With SSMs [14.42424591513825]
  MambaCSR is a framework based on Mamba for the challenging compressed image super-resolution (CSR) task. We propose an efficient dual-interleaved scanning paradigm (DIS) for CSR, which is composed of two scanning strategies. Results on multiple benchmarks have shown the great performance of our MambaCSR in the compressed image super-resolution task.
  arXiv  Detail & Related papers  (2024-08-21T16:30:45Z)
• Efficient Visual State Space Model for Image Deblurring [83.57239834238035]
  Convolutional neural networks (CNNs) and Vision Transformers (ViTs) have achieved excellent performance in image restoration. We propose a simple yet effective visual state space model (EVSSM) for image deblurring.
  arXiv  Detail & Related papers  (2024-05-23T09:13:36Z)
• RS-Mamba for Large Remote Sensing Image Dense Prediction [58.12667617617306]
  We propose the Remote Sensing Mamba (RSM) for dense prediction tasks in large VHR remote sensing images. RSM is specifically designed to capture the global context of remote sensing images with linear complexity. Our model achieves better efficiency and accuracy than transformer-based models on large remote sensing images.
  arXiv  Detail & Related papers  (2024-04-03T12:06:01Z)
• Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation [63.15257949821558]
  Referring Remote Sensing Image (RRSIS) is a new challenge that combines computer vision and natural language processing. Traditional Referring Image (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery. We introduce the Rotated Multi-Scale Interaction Network (RMSIN), an innovative approach designed for the unique demands of RRSIS.
  arXiv  Detail & Related papers  (2023-12-19T08:14:14Z)
• An Empirical Study of Remote Sensing Pretraining [117.90699699469639]
  We conduct an empirical study of remote sensing pretraining (RSP) on aerial images. RSP can help deliver distinctive performances in scene recognition tasks. RSP mitigates the data discrepancies of traditional ImageNet pretraining on RS images, but it may still suffer from task discrepancies.
  arXiv  Detail & Related papers  (2022-04-06T13:38:11Z)
• Homography augumented momentum constrastive learning for SAR image retrieval [3.9743795764085545]
  We propose a deep learning-based image retrieval approach using homography transformation augmented contrastive learning. We also propose a training method for the DNNs induced by contrastive learning that does not require any labeling procedure.
  arXiv  Detail & Related papers  (2021-09-21T17:27:07Z)
• Advances in Deep Learning for Hyperspectral Image Analysis--Addressing Challenges Arising in Practical Imaging Scenarios [7.41157183358269]
  We will review advances in the community that leverage deep learning for robust hyperspectral image analysis. challenges include limited ground truth and high dimensional nature of the data. Specifically, we will review unsupervised, semi-supervised and active learning approaches to image analysis.
  arXiv  Detail & Related papers  (2020-07-16T19:51:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.