Change detection is an essential and widely utilized task in remote sensing that aims to detect and analyze changes occurring in the same geographical area over time, which has broad applications in urban development, agricultural surveys, and land cover monitoring. Detecting changes in remote sensing images is a complex challenge due to various factors, including variations in image quality, noise, registration errors, illumination changes, complex landscapes, and spatial heterogeneity. In recent years, deep learning has emerged as a powerful tool for feature extraction and addressing these challenges. Its versatility has resulted in its widespread adoption for numerous image-processing tasks. This paper presents a comprehensive survey of significant advancements in change detection for remote sensing images over the past decade. We first introduce some preliminary knowledge for the change detection task, such as problem definition, datasets, evaluation metrics, and transformer basics, as well as provide a detailed taxonomy of existing algorithms from three different perspectives: algorithm granularity, supervision modes, and frameworks in the Methodology section. This survey enables readers to gain systematic knowledge of change detection tasks from various angles. We then summarize the state-of-the-art performance on several dominant change detection datasets, providing insights into the strengths and limitations of existing algorithms. Based on our survey, some future research directions for change detection in remote sensing are well identified. This survey paper sheds some light the topic for the community and will inspire further research efforts in the change detection task.

Deep learning has profoundly transformed remote sensing, yet prevailing architectures like Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) remain constrained by critical trade-offs: CNNs suffer from limited receptive fields, while ViTs grapple with quadratic computational complexity, hindering their scalability for high-resolution remote sensing data. State Space Models (SSMs), particularly the recently proposed Mamba architecture, have emerged as a paradigm-shifting solution, combining linear computational scaling with global context modeling. This survey presents a comprehensive review of Mamba-based methodologies in remote sensing, systematically analyzing about 120 Mamba-based remote sensing studies to construct a holistic taxonomy of innovations and applications. Our contributions are structured across five dimensions: (i) foundational principles of Vision Mamba architectures, (ii) micro-architectural advancements such as adaptive scan strategies and hybrid SSM formulations, (iii) macro-architectural integrations, including CNN–Transformer–Mamba hybrids and frequency-domain adaptations, (iv) rigorous benchmarking against state-of-the-art methods in multiple application tasks, such as object detection, semantic segmentation, change detection, etc. and (v) critical analysis of unresolved challenges with actionable future directions. By bridging the gap between SSM theory and remote sensing practice, this survey establishes Mamba as a transformative framework for remote sensing analysis. To our knowledge, this paper is the first systematic review of Mamba architectures in remote sensing. Our work provides a structured foundation for advancing research in remote sensing systems through SSM-based methods. We curate an open-source GitHub repository to foster community-driven advancements.

Optical coherence tomography angiography (OCTA) offers critical insights into the retinal vascular system, yet its full potential is hindered by challenges in precise image segmentation. Current methodologies struggle with imaging artifacts and clarity issues, particularly under low-light conditions and when using various high-speed CMOS sensors. These challenges are particularly pronounced when diagnosing and classifying diseases such as branch vein occlusion (BVO). To address these issues, we have developed a novel network based on topological structure generation, which transitions from superficial to deep retinal layers to enhance OCTA segmentation accuracy. Our approach not only demonstrates improved performance through qualitative visual comparisons and quantitative metric analyses but also effectively mitigates artifacts caused by low-light OCTA, resulting in reduced noise and enhanced clarity of the images. Furthermore, our system introduces a structured methodology for classifying BVO diseases, bridging a critical gap in this field. The primary aim of these advancements is to elevate the quality of OCTA images and bolster the reliability of their segmentation. Initial evaluations suggest that our method holds promise for establishing robust, fine-grained standards in OCTA vascular segmentation and analysis.

West syndrome is a persistent disease with common, age-dependent, severe neurological disorder that poses a substantial risk to intellectual and motor deficits of children. Researches on intelligent seizure detection method for West syndrome can help doctors make effective judgments. In this paper, we propose an infantile spasm seizure detection algorithm based on hybrid optimization of entropy of entropy (EoE) and Shannon entropy (SE). Different from traditional EoE and SE, fusion features are extracted from optimal division of EoE (ODEoE) and optimal SE (OSE) by considering the epoch length and the number of subepochs in an epoch jointly. In addition, multivariate entropy parameterization via enhanced multi-strategy sparrow algorithm (MEPEM-SSA) is employed for choosing the ideal parameters. Finally, the data from Zhejiang University School of Medicine Children’s Hospital (ZUCH) dataset, CHB-MIT dataset, and Siena scalp electroencephalogram (EEG) dataset are extracted using EEG sensors, and a 10-fold cross validation evaluation is performed on these EEG data from West syndrome patients. The experimental results on the ZUCH dataset show that the mean values of precision, specificity, sensitivity and F1 score of the algorithm are 98.08%, 97.93%, 98.46% and 98.09% respectively. On the CHB-MIT dataset, the mean values of precision, specificity, sensitivity and F1 score reach 98.76%, 99.27%, 97.94% and 98.59%, respectively. And on the Siena scalp EEG dataset, the mean values of precision, specificity, sensitivity and F1 score reach 98.33%, 98.04%, 98.39% and 98.20%, respectively.

Smoke and fire detection is vital for timely fire alarms, but traditional sensor-based methods are often unresponsive and costly. While deep learning-based methods offer promise using aerial images and surveillance images, the scarcity and limited diversity of smoke-and-fire-related image data hinder model accuracy and generalization. Alpha composition, blending foreground and background using per-pixel alpha values (transparency parameters stored in the alpha channel alongside RGB channels), can effectively augment smoke and fire image datasets. Since image matting algorithms compute these alpha values, the quality of the alpha composition directly depends on the performance of the smoke and fire matting methods. However, due to the lack of smoke and fire image matting datasets for model training, existing image matting methods exhibit significant errors in predicting the alpha values of smoke and fire targets, leading to unrealistic composite images. Therefore, to address these above issues, the main research contributions of this paper are as follows: (1) Construction of a high-precision, large-scale smoke and fire image matting dataset, SFMatting-800. The images in this dataset are sourced from diverse real-world scenarios. It provides precise foreground opacity values and attribute annotations. (2) Evaluation of existing image matting baseline methods. Based on the SFMatting-800 dataset, traditional, trimap-based deep learning and trimap-free deep learning matting methods are evaluated to identify their strengths and weaknesses, providing a benchmark for improving future smoke and fire matting methods. (3) Proposal of a deep learning-based trimap-free smoke and fire image matting network, SFMattingNet, which takes the original image as input without using trimaps. Taking into account the unique characteristics of smoke and fire, the network incorporates a non-rigid object feature extraction module and a spatial awareness module, achieving improved performance. Compared to the suboptimal approach, MODNet, our SFMattingNet method achieved an average error reduction of 12.65% in the smoke and fire matting task.