We present Open-CD, a change detection toolbox that contains a rich set of change detection methods as well as related components and modules. The toolbox started from a series of open source general vision task tools, including OpenMMLab Toolkits, PyTorch Image Models (Timm), etc. It gradually evolves into a unified platform that covers many popular change detection methods and contemporary modules. It not only includes training and inference codes, but also provides some useful scripts for data analysis. We believe this toolbox is by far the most comprehensive change detection toolbox. In this report, we introduce the features, supported methods and applications of Open-CD. In addition, we also conduct a benchmarking study on different methods and components. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to re-implement existing methods and develop their own new change detectors. Code and models are available at https://github.com/likyoo/open-cd.

Although Mamba-based models have demonstrated great potential in hyperspectral image (HSI) classification, existing approaches often rely on patch-wise inputs, causing redundant computation and limiting global spectral–spatial modeling, while the absence of hierarchical representation and explicit interaction further constrains fine-grained fusion. To address these limitations, we propose a Progressive Hybrid Mamba Model (PHDMamba), which is designed to progressively capture long-range spectral-spatial contextual dependencies and gradually fuse spectral and spatial information through adaptive feature interaction, ultimately yielding a unified representation. Specifically, we develop a Progressive Hybrid Mamba Module, which performs stage-wise modeling of long-range dependencies along both spectral and spatial dimensions. In addition, a dedicated Spectral-Spatial Interaction Module is introduced to adaptively integrate contextual spectral and spatial features. Experimental results on three widely used HSI benchmark datasets demonstrate that the proposed method achieves superior classification performance compared to existing approaches. The implementation code will be released publicly.

Accurate hyperspectral image (HSI) interpretation is critical for providing valuable insights into various earth observation-related applications such as urban planning, precision agriculture, and environmental monitoring. However, existing HSI processing methods are predominantly task-specific and scene-dependent, which severely limits their ability to transfer knowledge across tasks and scenes, thereby reducing the practicality in real-world applications. To address these challenges, we present HyperSIGMA, a vision transformer-based foundation model that unifies HSI interpretation across tasks and scenes, scalable to over one billion parameters. To overcome the spectral and spatial redundancy inherent in HSIs, we introduce a novel sparse sampling attention (SSA) mechanism, which effectively promotes the learning of diverse contextual features and serves as the basic block of HyperSIGMA. HyperSIGMA integrates spatial and spectral features using a specially designed spectral enhancement module. In addition, we construct a large-scale hyperspectral dataset, HyperGlobal-450K, for pre-training, which contains about 450 K hyperspectral images, significantly surpassing existing datasets in scale. Extensive experiments on various high-level and low-level HSI tasks demonstrate HyperSIGMA's versatility and superior representational capability compared to current state-of-the-art methods. Moreover, HyperSIGMA shows significant advantages in scalability, robustness, cross-modal transferring capability, real-world applicability, and computational efficiency.

Convolutional neural networks (CNNs) and Transformers have made impressive progress in the field of remote sensing change detection (CD). However, both architectures have inherent shortcomings: CNN is 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 (SSMs), 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 article, 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 CD (BCD), semantic CD (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 spatiotemporal relationship modeling mechanisms, which can be naturally combined with the Mamba architecture and fully utilize its attribute to achieve spatiotemporal interaction of multitemporal 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 is available at:https://github.com/ChenHongruixuan/MambaCD.

A high-precision feature extraction model is crucial for change detection. In the past, many deep learning-based supervised change detection methods learned to recognize change feature patterns from a large number of labelled bi-temporal images, whereas labelling bi-temporal remote sensing images is very expensive and often time-consuming. Therefore, we propose a coarse-to-fine semi-supervised change detection method based on consistency regularization (C2F-SemiCD), which includes a coarse-to-fine change detection network with a multi-scale attention mechanism(C2FNet) and a semi-supervised update method. Among them, the C2FNet network "gradually" completes the extraction of change features from coarse-grained to fine-grained through multi-scale feature fusion, channel attention mechanism, spatial attention mechanism, global context module, feature refine module, initial aggregation module, and final aggregation module. The semi-supervised update method uses the mean teacher method. The parameters of the student model are updated to the parameters of the teacher Model by using the exponential moving average (EMA) method. Through extensive experiments on three datasets and meticulous ablation studies, including crossover experiments across datasets, we verify the significant effectiveness and efficiency of the proposed C2F-SemiCD method. The code will be open at: https://github.com/ChengxiHAN/C2F-SemiCD-and-C2FNet.

Very-high-resolution (VHR) remote sensing (RS) image change detection (CD) has been a challenging task for its very rich spatial information and sample imbalance problem. In this paper, we have proposed a hierarchical change guiding map network (HCGMNet) for VHR RS change detection. The model uses hierarchical convolution operations to extract multi-scale features, continuously merges multi-scale features layer by layer to improve the expression of global and local information, and guides the model to gradually refine edge features and comprehensive performance by a change guide module (CGM), which is a self-attention with changing guide map. Extensive experiments on two CD datasets show that the proposed HCGMNet architecture achieves better CD performance than existing state-of-the-art (SOTA) CD methods.