Mamba, with its advantages of global perception and linear complexity, has been widely applied to identify changes of the target regions within the remote sensing (RS) images captured under complex scenarios and varied conditions. However, existing remote sensing change detection (RSCD) approaches based on Mamba frequently struggle to effectively perceive the inherent locality of change regions as they direct flatten and scan RS images (i.e., the features of the same region of changes are not distributed continuously within the sequence but are mixed with features from other regions throughout the sequence). In this paper, we propose a novel locally adaptive SSM-based approach, termed CD-Lamba, which effectively enhances the locality of change detection while maintaining global perception. Specifically, our CD-Lamba includes a Locally Adaptive State-Space Scan (LASS) strategy for locality enhancement, a Cross-Temporal State-Space Scan (CTSS) strategy for bi temporal feature fusion, and a Window Shifting and Perception (WSP) mechanism to enhance interactions across segmented windows. These strategies are integrated into a multi-scale Cross-Temporal Locally Adaptive State-Space Scan (CT-LASS) module to effectively highlight changes and refine changes' representations feature generation. CD-Lamba significantly enhances local-global spatio-temporal interactions in bi-temporal images, offering improved performance in RSCD tasks. Extensive experimental results show that CD-Lamba achieves state-of-the art performance on four benchmark datasets with a satisfactory efficiency-accuracy trade-off. Our code is publicly available at https://github.com/xwmaxwma/rschange.

In complex scenes and varied conditions, effectively integrating spatial-temporal context is crucial for accurately identifying changes. However, current RS-CD methods lack a balanced consideration of performance and efficiency. CNNs lack global context, Transformers are computationally expensive, and Mambas face CUDA dependence and local correlation loss. In this paper, we propose CDXLSTM, with a core component that is a powerful XLSTM-based feature enhancement layer, integrating the advantages of linear computational complexity, global context perception, and strong interpret-ability. Specifically, we introduce a scale-specific Feature Enhancer layer, incorporating a Cross-Temporal Global Perceptron customized for semantic-accurate deep features, and a Cross-Temporal Spatial Refiner customized for detail-rich shallow features. Additionally, we propose a Cross-Scale Interactive Fusion module to progressively interact global change representations with spatial responses. Extensive experimental results demonstrate that CDXLSTM achieves state-of-the-art performance across three benchmark datasets, offering a compelling balance between efficiency and accuracy. Code is available at https://github.com/xwmaxwma/rschange.

Mamba, with its advantages of global perception and linear complexity, has been widely applied to identify changes of the target regions within the remote sensing (RS) images captured under complex scenarios and varied conditions. However, existing remote sensing change detection (RSCD) approaches based on Mamba frequently struggle to effectively perceive the inherent locality of change regions as they direct flatten and scan RS images (i.e., the features of the same region of changes are not distributed continuously within the sequence but are mixed with features from other regions throughout the sequence). In this paper, we propose a novel locally adaptive SSM-based approach, termed CD-Lamba, which effectively enhances the locality of change detection while maintaining global perception. Specifically, our CD-Lamba includes a Locally Adaptive State-Space Scan (LASS) strategy for locality enhancement, a Cross-Temporal State-Space Scan (CTSS) strategy for bi-temporal feature fusion, and a Window Shifting and Perception (WSP) mechanism to enhance interactions across segmented windows. These strategies are integrated into a multi-scale Cross-Temporal Locally Adaptive State-Space Scan (CT-LASS) module to effectively highlight changes and refine changes' representations feature generation. CD-Lamba significantly enhances local-global spatio-temporal interactions in bi-temporal images, offering improved performance in RSCD tasks. Extensive experimental results show that CD-Lamba achieves state-of-the-art performance on four benchmark datasets with a satisfactory efficiency-accuracy trade-off. Our code is publicly available at https://github.com/xwmaxwma/rschange.

Remote sensing change detection (RSCD) aims to compare two or more images recorded for the same area but taken at different time stamps to quantitatively and qualitatively assess changes in geographical entities and environmental factors. Mainstream models usually built on pixel-by-pixel change detection paradigms, which cannot tolerate the diversity of changes due to complex scenes and variation in imaging conditions. Recently, some approaches have attempted to perform RSCD directly based on mask classification. However, they are inefficient and perform sub-optimally. In this paper, we provide an in-depth analysis of the change characteristic of RSCD, and further proposes the corresponding: 1) meta-architecture CDMask and 2) an enhanced instance network CDMaskFormer. Components of CDMask include Siamese backbone, change extractor, pixel decoder, transformer decoder and change customized normalized detector, which accommodates the change characteristics of the RSCD and ensures the proper functioning of the mask detection paradigm. Since the change query can be adaptively updated based on the bi-temporal feature content, the proposed CDMask can adapt to different latent data distributions, thus accurately identifying regions of interest changes in complex scenarios. Then, we further propose an enhanced instance network CDMaskFormer for the change detection task, which includes: (i) a Spatial-temporal convolutional attention-based instantiated change extractor to capture spatio-temporal context simultaneously with lightweight operations; and (ii) a scene-guided axial attention-instantiated transformer decoder to extract more spatial details. State-of-the-art performance of CDMaskFormer is achieved on five benchmark datasets with a satisfactory efficiency-accuracy trade-off. In particular, We construct a GAZACD dataset to explore the performance of CDMaskFormer in the real world. By analyzing 4353 pairs of images of the Gaza surface during and after the conflict, CDMaskFormer is able to effectively reveal the surface changes that occurred in the Gaza Strip and guide the post-disaster reconstruction. Code and dataset are available at https://github.com/xwmaxwma/rschange.

The detection of abnormal behavior has consistently garnered significant attention. Conventional methods employ vision-based dual-stream networks or 3D convolutions to represent spatio-temporal information in video sequences to identify normal and pathological behaviors. Nonetheless, these methodologies generally employ datasets balanced across data categories and consist solely of two classifications. In actuality, anomalous behaviors frequently display multi-category characteristics, with each category’s distribution demonstrating a pronounced long-tail phenomenon. This paper presents a video-based technique for detecting multi-category abnormal behavior, termed the Spatio-Temporal Fusion–Temporal Difference Network (STF-TDN). The system first employs a temporal difference network (TDN) model to encapsulate movie temporal dynamics via local and global modeling. To enhance recognition performance, this study develops a feature fusion module—Spatial-Temporal Fusion (STF)—which augments the model’s representational capacity by amalgamating spatial and temporal data. Furthermore, given the long-tailed distribution characteristics of the datasets, this study employs focused loss rather than the conventional cross-entropy loss function to enhance the model’s recognition capability for under-represented categories. We perform comprehensive experiments and ablation studies on two datasets. Precision is 96.3% for the Violence5 dataset and 87.5% for the RWF-2000 dataset. The results of the experiment indicate the enhanced efficacy of the proposed strategy in detecting anomalous behavior.