Pith. sign in

REVIEW 9 cited by

ChangeMamba: Remote Sensing Change Detection With Spatiotemporal State Space Model

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2404.03425 v7 pith:352S4W3V submitted 2024-04-04 eess.IV cs.AIcs.CV

Hongruixuan Chen , Jian Song , Chengxi Han , Junshi Xia , Naoto Yokoya This is my paper · ORCID
classification eess.IVcs.AIcs.CV
keywords architecturechangemambadetectionarchitecturesframeworksremotesensing
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

Convolutional neural networks (CNN) and Transformers have made impressive progress in the field of remote sensing change detection (CD). However, both architectures have inherent shortcomings: CNN are constrained by a limited receptive field that may hinder their ability to capture broader spatial contexts, while Transformers are computationally intensive, making them costly to train and deploy on large datasets. Recently, the Mamba architecture, based on state space models, has shown remarkable performance in a series of natural language processing tasks, which can effectively compensate for the shortcomings of the above two architectures. In this paper, we explore for the first time the potential of the Mamba architecture for remote sensing CD tasks. We tailor the corresponding frameworks, called MambaBCD, MambaSCD, and MambaBDA, for binary change detection (BCD), semantic change detection (SCD), and building damage assessment (BDA), respectively. All three frameworks adopt the cutting-edge Visual Mamba architecture as the encoder, which allows full learning of global spatial contextual information from the input images. For the change decoder, which is available in all three architectures, we propose three spatio-temporal relationship modeling mechanisms, which can be naturally combined with the Mamba architecture and fully utilize its attribute to achieve spatio-temporal interaction of multi-temporal features, thereby obtaining accurate change information. On five benchmark datasets, our proposed frameworks outperform current CNN- and Transformer-based approaches without using any complex training strategies or tricks, fully demonstrating the potential of the Mamba architecture in CD tasks. Further experiments show that our architecture is quite robust to degraded data. The source code will be available in https://github.com/ChenHongruixuan/MambaCD

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. AtrousMamaba: An Atrous-Window Scanning Visual State Space Model for Remote Sensing Change Detection

    cs.CV 2025-07 conditional novelty 6.0 of 10

    An atrous-window scanning strategy improves Mamba-based change detection on six remote sensing benchmarks, showing visual state space models can capture fine local details alongside global context.

  2. ECP-Mamba: An Efficient Multi-scale Self-supervised Contrastive Learning Method with State Space Model for PolSAR Image Classification

    cs.CV 2025-06 conditional novelty 6.0 of 10

    ECP-Mamba, a Mamba-based network with a spiral scan and multi-scale self-distillation, reports state-of-the-art PolSAR image classification accuracy at label rates as low as 0.2%.

  3. CD-Lamba: Boosting Remote Sensing Change Detection via a Cross-Temporal Locally Adaptive State Space Model

    cs.CV 2025-01 conditional novelty 6.0 of 10

    CD-Lamba introduces adaptive top-k window selection and pixel-wise cross-temporal scanning for Mamba-based remote sensing change detection, achieving state-of-the-art F1 on WHU-CD, SYSU-CD, DSIFN-CD, and CLCD.

  4. Robust Change Captioning in Remote Sensing: SECOND-CC Dataset and MModalCC Framework

    cs.CV 2025-01 conditional novelty 6.0 of 10

    A new dataset and a multimodal attention model improve remote sensing change captioning, but only when ground-truth semantic maps are supplied as input.

  5. AVS-Mamba: Exploring Temporal and Multi-modal Mamba for Audio-Visual Segmentation

    cs.CV 2025-01 reject novelty 6.0 of 10

    AVS-Mamba applies Mamba with temporal and cross-modal scanning to audio-visual segmentation, reporting top scores on AVSBench-object but not on AVSBench-semantic with the stronger backbone.

  6. Improving Zero-Shot Object-Level Change Detection by Incorporating Visual Correspondence

    cs.CV 2025-01 conditional novelty 6.0 of 10

    A contrastive matching loss plus homography-based alignment and Hungarian matching improves zero-shot change detection and predicts correspondences between detected changes.

  7. Directing Mamba to Complex Textures: An Efficient Texture-Aware State Space Model for Image Restoration

    cs.CV 2025-01 conditional novelty 5.0 of 10

    TAMambaIR introduces a texture-aware state space model that prioritizes high-texture patches, achieving modest gains on restoration benchmarks with lower FLOPs than comparable Mamba models.

  8. Semantic-CD: Remote Sensing Image Semantic Change Detection towards Open-vocabulary Setting

    cs.CV 2025-01 conditional novelty 5.0 of 10

    Semantic-CD couples binary and semantic change detection in remote sensing imagery via CLIP text-guided cost volumes, reporting state-of-the-art scores on the SECOND dataset.

  9. SegChange-R1: LLM-Augmented Remote Sensing Change Detection

    cs.CV 2025-06 reject novelty 3.0 of 10

    SegChange-R1 combines a Swin encoder with a Phi-1.5 text encoder and a linear-attention BEV module, reporting gains on three change-detection benchmarks and a new drone-view dataset.

Pith tools