Search Results (267)

Change detection in remote sensing imagery plays a vital role in land use analysis, disaster assessment, and ecological monitoring. However, existing remote sensing change detection methods often lack a structured and tightly coupled interaction paradigm to jointly reconcile multi-scale representation, bi-temporal discrimination, and fine-grained boundary modeling under practical computational constraints. To address this fundamental challenge, we propose a Multi-scale Interaction and Supervised Attention Network (MISANet). To improve the model’s ability to perceive changes at multiple scales, we design a Progressive Multi-Scale Feature Fusion Module (PMFFM), which employs a progressive fusion strategy to effectively integrate multi-granular cross-scale features. To enhance the interaction between bi-temporal features, we introduce a Difference-guided Gated Attention Interaction (DGAI) module. This component leverages difference information between the two time phases and employs a gating mechanism to retain fine-grained details, thereby improving semantic consistency. Furthermore, to guide the model’s focus on change regions, we design a Supervised Attention Decoder Module (SADM). This module utilizes a channel–spatial joint attention mechanism to reweight the feature maps. In addition, a deep supervision strategy is incorporated to direct the model’s attention toward both fine-grained texture differences and high-level semantic changes during training. Experiments conducted on the LEVIR-CD, SYSU-CD, and GZ-CD datasets demonstrate the effectiveness of our method, achieving F1-scores of 91.19%, 82.25%, and 88.35%, respectively. Compared with the state-of-the-art BASNet model, MISANet achieves performance gains of 0.50% F1 and 0.85% IoU on LEVIR-CD, 2.13% F1 and 3.02% IoU on SYSU-CD, and 1.28% F1 and 2.03% IoU on GZ-CD. The proposed method demonstrates strong generalization capabilities and is applicable to various complex change detection scenarios. Full article

Understanding complex change scenes is a crucial challenge in remote sensing field. Remote sensing image change captioning (RSICC) task has emerged as a promising approach to translate appeared changes between bi-temporal remote sensing images into textual descriptions, enabling users to make accurate decisions. Current RSICC methods frequently encounter difficulties in consistency for contextual awareness and semantic prior guidance. Therefore, this study explores difference semantic prior guidance network to reason context-rich sentence for capturing appeared vision changes. Specifically, the context-aware difference module is introduced to guarantee the consistency of unchanged/changed context features, strengthening multi-level changed information to improve the ability of semantic change feature representation. Moreover, to effectively mine higher-level cognition ability to reason salient/weak changes, we employ difference comprehending with shallow change information to realize semantic change knowledge learning. In addition, the designed parallel cross refined attention in Transformer decoder can balance vision difference and semantic knowledge for implicit knowledge distilling, enabling fine-grained perception changes of semantic details and reducing pseudochanges. Compared with advanced algorithms on the LEVIR-CC and Dubai-CC datasets, experimental results validate the outstanding performance of the designed model in RSICC tasks. Notably, on the LEVIR-CC dataset, it reaches a CIDEr score of 143.34%, representing a 3.11% improvement over the most competitive SAT-cap. Full article

To address the issues of inadequate performance and excessive interaction costs when handling large-scale and complex-shaped change areas with existing interaction forms, this paper proposes RSICDNet, an interactive change detection (ICD) model with regional scribble interaction. In this framework, regional scribble interaction is introduced for the first time to provide rich spatial prior information for accurate ICD. Specifically, RSICDNet first employs an interaction processing network to extract interactive features, and subsequently utilizes the High-Resolution Network (HRNet) backbone to extract features from bi-temporal remote sensing images concatenated along the channel dimension. To effectively integrate these two information streams, an Interaction Fusion and Refinement Module (IFRM) is proposed, which injects the spatial priors from the interactive features into the high-level semantic features. Finally, an Object Contextual Representation (OCR) module is applied to further refine feature representations, and a lightweight segmentation head is used to generate final change map. Furthermore, a human–computer ICD application has been developed based on RSICDNet, significantly enhancing its potential for practical deployment. To validate the effectiveness of the proposed RSICDNet, extensive experiments are conducted against mainstream interactive deep learning models on the WHU-CD, LEVIR-CD, and CLCD datasets. The quantitative results demonstrate that RSICDNet achieves optimal Number of Interactions (NoI) metrics across all three datasets. Specifically, its NoI80 values reach 1.15, 1.45, and 3.42 on the WHU-CD, LEVIR-CD, and CLCD datasets, respectively. The qualitative results confirm a clear advantage for RSICDNet, which consistently delivers visually superior outcomes using the same or often fewer interactions. Full article

Semantic change detection (SCD) effectively captures ground object transition information within change regions, delivering more comprehensive and detailed results than binary change detection (BCD) tasks. The existing multi-task SCD models enable parallel processing of segmentation and BCD of bi-temporal remote sensing images, but they still have shortcomings in feature mining, interaction, and cross-task transfer. To address these limitations, a multi-branch feature interaction network (MBFI-Net) is proposed. MBFI-Net designs parallel encoding branches with attention mechanisms that enhance semantic change perception by jointly modeling global contextual patterns and local details. In addition, MBFI-Net proposes bi-temporal feature interaction (BTFI) and cross-task feature transfer (CTFT) modules to improve feature diversity and representativeness, and combines with prior logical relationship constraints to improve SCD performance. Comparative and ablation studies on the SECOND and Landsat-SCD datasets highlight the superiority and robustness of MBFI-Net, which achieves SeKs of 0.2117 and 0.5543, respectively. Furthermore, MBFI-Net strikes a balance between SCD results and model complexity and has superior detection performance for semantic change categories with a small proportion. Full article

Background/Objectives: The aim of this study was to simultaneously evaluate alterations in apoptosis-related biomarker gene expression accompanied by electroconvulsive therapy (ECT) in treatment-resistant depression (TRD) patients. Methods: A total of 25 subjects (15 healthy controls; 10 TRD patients) were initially tested for baseline values of relative mRNA expression of apoptosis-related markers (Bax, Bcl-2, p53, and cytochrome c) in peripheral blood samples and MADRS score. Results: Healthy subjects showed significantly lower values in MADRS, and Bax and p53, with increased Bcl-2 expression. The four-week ECT protocol (bitemporal, three sessions per week, with MADRS evaluation and blood sampling after each week) in TRD patients resulted in a concomitant significant decrease in MADRS, Bax, and p53 and an increase in Bcl-2 expression. Conclusions: Our results confirmed that the benefits observed by clinical outcome may also be attributed to the anti-apoptotic impact of ECT. Full article

Timely detection and handling of substation defects plays a foundational role in ensuring the stable operation of power systems. Existing substation defect detection methods fail to make full use of the temporal information contained in substation inspection samples, resulting in problems such as weak generalization ability and susceptibility to background interference. To address these issues, a change attention guided substation defect detection algorithm (CAG-Net) based on a dual-temporal encoder–decoder framework is proposed. The encoder module employs a Siamese backbone network composed of efficient local-global context aggregation modules to extract multi-scale features, balancing local details and global semantics, and designs a change attention guidance module that takes feature differences as attention weights to dynamically enhance the saliency of defect regions and suppress background interference. The decoder module adopts an improved FPN structure to fuse high-level and low-level features, supplement defect details, and improve the model’s ability to detect small targets and multi-scale defects. Experimental results on the self-built substation multi-phase defect dataset (SMDD) show that the proposed method achieves 81.76% in terms of mAP, which is 3.79% higher than that of Faster R-CNN and outperforms mainstream detection models such as GoldYOLO and YOLOv10. Ablation experiments and visualization analysis demonstrate that the method can effectively focus on defect regions in complex environments, improving the positioning accuracy of multi-scale targets. Full article

Change detection (CD) with synthetic aperture radar (SAR) images remains pivotal for environmental monitoring and disaster management. Deep learning has powerful feature extraction capabilities for CD, but suffers from complex architectures and limited interpretability. While BLSs demonstrate advantages in structural simplicity and interpretability, their feature representation capacity remains constrained. In high-precision CD with SAR images, strong feature representation capability is required, along with an uncomplicated framework and high interpretability. Therefore, a novel paradigm named PC-BiBL is proposed which achieves seamless integration of deep learning and broad learning. On the one hand, it employs a hierarchical cross-convolutional encoding (HCCE) module that uses pseudo-random cross-convolution (PCConv) for hierarchical cross-feature representation, aggregating contextual information. PCConv is an untrained convolution layer, which can utilize specialized pseudo-random kernels to extract features from bitemporal SAR images. On the other hand, since back-propagation algorithms are not required, the features can be directly fed into the bifurcated broad learning (BiBL) module for node expansion and direct parameter computation. BiBL constructs dual-branch nodes and computes their difference nodes, explicitly fusing bitemporal features while highlighting change information—an advancement over traditional BLS. Experiments on five SAR datasets demonstrate the state-of-the-art performance of PC-BiBL, surpassing existing methods in accuracy and robustness. Quantitative metrics and visual analyses confirm its superiority in handling speckle noise and preserving boundary information. Full article

Cropland change detection (CD) in high-resolution remote sensing images is critical for cropland protection and food security. However, style differences caused by inconsistent imaging conditions (such as season and illumination) and ground object scale differences often lead to high numbers of false and missed detections. Existing approaches, predominantly relying on spatial domain features and a multiscale framework, struggle to address these issues effectively. Therefore, we propose FDFENet, incorporating a Frequency Domain Feature Exchange Module (FFEM) that unifies image styles by swapping the low-frequency components of bitemporal features. A Frequency Domain Aggregation Distribution Module (FDADM) is also introduced as a comparative alternative for handling style discrepancies. Subsequently, a Multiscale Feature Enhancement Module (MSFEM) strengthens feature representation, while a Multiscale Change Perception Module (MSCPM) suppresses non-change information, and the two modules work cooperatively to improve detection sensitivity to multiscale ground objects. Compared with the FDADM, the FFEM exhibits superior parameter efficiency and engineering stability, making it more suitable as the primary solution for long-term deployment. Evaluations on four CD datasets (CLCD, GFSWCLCD, LuojiaSETCLCD, and HRCUSCD) demonstrate that FDFENet outperformed 13 state-of-the-art methods, achieving F1 and IOU scores of 77.09% and 62.72%, 81.81% and 73.63%, 74.47% and 59.32%, and 75.95% and 61.23%, respectively. This demonstrates FDFENet’s effectiveness in addressing style differences and ground object scale differences, enabling high-precision cropland monitoring to support food security and sustainable cropland management. Full article

Surface crack detection and temporal evolution analysis are fundamental tasks in remote sensing and photogrammetry, providing critical information for slope stability assessment, infrastructure safety inspection, and long-term geohazard monitoring. However, current unmanned aerial vehicle (UAV)-based crack detection pipelines typically treat spatial detection and temporal change analysis as separate processes, leading to weak geometric consistency across time and limiting the interpretability of crack evolution patterns. To overcome these limitations, we propose the Longitudinal Crack Fitting Network (LCFNet), a unified and physically interpretable framework that achieves, for the first time, integrated time-series crack detection and evolution analysis from UAV remote sensing imagery. At its core, the Longitudinal Crack Fitting Convolution (LCFConv) integrates Fourier-series decomposition with affine Lie group convolution, enabling anisotropic feature representation that preserves equivariance to translation, rotation, and scale. This design effectively captures the elongated and oscillatory morphology of surface cracks while suppressing background interference under complex aerial viewpoints. Beyond detection, a Lie-group-based Temporal Crack Change Detection (LTCCD) module is introduced to perform geometrically consistent matching between bi-temporal UAV images, guided by a partial differential equation (PDE) formulation that models the continuous propagation of surface fractures, providing a bridge between discrete perception and physical dynamics. Extensive experiments on the constructed UAV-Filiform Crack Dataset (10,588 remote sensing images) demonstrate that LCFNet surpasses advanced detection frameworks such as You only look once v12 (YOLOv12), RT-DETR, and RS-Mamba, achieving superior performance (mAP_50:95 = 75.3%, F1 = 85.5%, and CDR = 85.6%) while maintaining real-time inference speed (88.9 FPS). Field deployment on a UAV–IoT monitoring platform further confirms the robustness of LCFNet in multi-temporal remote sensing applications, accurately identifying newly formed and extended cracks under varying illumination and terrain conditions. This work establishes the first end-to-end paradigm that unifies spatial crack detection and temporal evolution modeling in UAV remote sensing, bridging discrete deep learning inference with continuous physical dynamics. The proposed LCFNet provides both algorithmic robustness and physical interpretability, offering a new foundation for intelligent remote sensing-based structural health assessment and high-precision photogrammetric monitoring. Full article

Accurate mapping of hurricane-induced damage is essential for guiding rapid disaster response and long-term recovery planning. This study evaluates the Three-Dimensional Multi-Attributes, Multiscale, Multi-Cloud (3DMASC) framework for semantic classification of pre- and post-hurricane Light Detection and Ranging (LiDAR) data, using Mexico Beach, Florida, as a case study following Hurricane Michael. The goal was to assess the framework’s ability to classify stable landscape features and detect damage-specific classes in a highly complex post-disaster environment. Bitemporal topo-bathymetric LiDAR datasets from 2017 (pre-event) and 2018 (post-event) were processed to extract more than 80 geometric, radiometric, and echo-based features at multiple spatial scales. A Random Forest classifier was trained on a 2.37 km² pre-hurricane area (Zone A) and evaluated on an independent 0.95 km² post-hurricane area (Zone B). Pre-hurricane classification achieved an overall accuracy of 0.9711, with stable classes such as ground, water, and buildings achieving precision and recall exceeding 0.95. Post-hurricane classification maintained similar accuracy; however, damage-related classes exhibited lower performance, with debris reaching an F1-score of 0.77, damaged buildings 0.58, and vehicles recording a recall of only 0.13. These results indicate that the workflow is effective for rapid mapping of persistent structures, with additional refinements needed for detailed damage classification. Misclassifications were concentrated along class boundaries and in structurally ambiguous areas, consistent with known LiDAR limitations in disaster contexts. These results demonstrate the robustness and spatial transferability of the 3DMASC–Random Forest approach for disaster mapping. Integrating multispectral data, improving small-object representation, and incorporating automated debris volume estimation could further enhance classification reliability, enabling faster, more informed post-disaster decision-making. By enabling rapid, accurate damage mapping, this approach supports sustainable disaster recovery, resource-efficient debris management, and resilience planning in hurricane-prone regions. Full article

Remote sensing image change captioning (RSICC) fuses computer vision and natural language processing to translate visual differences between bi-temporal remote sensing images into interpretable text, with applications in environmental monitoring, urban planning, and disaster assessment. Multimodal Large Language Models (MLLMs) boost RSICC performance but suffer from inefficient inference due to massive parameters, whereas lightweight models enable fast inference yet lack generalization across diverse scenes, which creates a critical timeliness-generalization trade-off. To address this, we propose the Dual-Constrained Transformer (DCT), an end-to-end lightweight RSICC model with three core modules and a decoder. Full-Level Feature Distillation (FLFD) transfers hierarchical knowledge from a pre-trained Dinov3 teacher to a Generalizable Lightweight Visual Encoder (GLVE), enhancing generalization while retaining compactness. Key Change Region Adaptive Weighting (KCR-AW) generates Region Difference Weights (RDW) to emphasize critical changes and suppress backgrounds. Hierarchical encoding and Difference weight Constrained Attention (HDC-Attention) refine multi-scale features via hierarchical encoding and RDW-guided noise suppression; these features are fused by multi-head self-attention and fed into a Transformer decoder for accurate descriptions. The DCT resolves three core issues: lightweight encoder generalization, key change recognition, and multi-scale feature-text association noise, achieving a dynamic balance between inference efficiency and description quality. Experiments on the public LEVIR-CC dataset show our method attains SOTA among lightweight approaches and matches advanced MLLM-based methods with only 0.98% of their parameters. Full article

Change detection (CD) in high-resolution remote sensing imagery is vital for resource monitoring and disaster assessment but faces challenges such as spatiotemporal heterogeneity, spectral variability, and computational inefficiency. This paper proposes an efficient CD method that hybridizes Convolutional Neural Networks (CNNs) and Transformers. A CNN backbone first extracts multi-level features from bi-temporal images. A Semantic Token Generator then compresses these features into compact, low-dimensional semantic tokens, reducing computational load. The core of our model is a novel cooperative encoder integrating a Spectral layer and an Attention layer. The Spectral layer enhances sensitivity to high-frequency components like edges and textures in the Fourier domain, while the Attention layer captures long-range semantic dependencies via self-attention. Furthermore, we introduce a Dynamic Tanh (DyT) module to replace conventional normalization layers, using learnable parameters to adaptively adjust activation thresholds, thereby improving training stability and computational efficiency. Comprehensive evaluations on the LEVIR-CD, WHU-CD, and DSIFN-CD benchmarks demonstrate that our method maintains high accuracy while reducing complexity, offering a practical solution for real-time CD in resource-limited environments. Full article

In war and explosion scenarios, buildings often suffer varying degrees of damage characterized by complex, irregular, and fragmented spatial patterns, posing significant challenges for remote sensing–based change detection. Additionally, the scarcity of high-quality datasets limits the development and generalization of deep learning approaches. To overcome these issues, we propose CMSNet, an end-to-end framework that integrates the structural priors of the Segment Anything Model (SAM) with the efficient temporal modeling and fine-grained representation capabilities of CNN–Mamba. Specifically, CMSNet adopts CNN–Mamba as the backbone to extract multi-scale semantic features from bi-temporal images, while SAM-derived visual priors guide the network to focus on building boundaries and structural variations. A Pre-trained Visual Prior-Guided Feature Fusion Module (PVPF-FM) is introduced to align and fuse these priors with change features, enhancing robustness against local damage, non-rigid deformations, and complex background interference. Furthermore, we construct a new RWSBD (Real-world War Scene Building Damage) dataset based on Gaza war scenes, comprising 42,732 annotated building damage instances across diverse scales, offering a strong benchmark for real-world scenarios. Extensive experiments on RWSBD and three public datasets (CWBD, WHU-CD, and LEVIR-CD+) demonstrate that CMSNet consistently outperforms eight state-of-the-art methods in both quantitative metrics (F1, IoU, Precision, Recall) and qualitative evaluations, especially in fine-grained boundary preservation, small-scale change detection, and complex scene adaptability. Overall, this work introduces a novel detection framework that combines foundation model priors with efficient change modeling, along with a new large-scale war damage dataset, contributing valuable advances to both research and practical applications in remote sensing change detection. Additionally, the strong generalization ability and efficient architecture of CMSNet highlight its potential for scalable deployment and practical use in large-area post-disaster assessment. Full article

Remote sensing image change captioning (RSICC) aims to describe changes between bi-temporal remote sensing (RS) images in natural language. The existing methods, typically based on traditional encoder–decoder architectures or multi-task collaboration, face limitations in terms of either their description accuracy or computational efficiency. To address these challenges, we propose the MTH-Net, a Mamba–Transformer Hybrid Network with joint spatiotemporal awareness. The model is built upon a symmetric Siamese network to extract comparable features from the input image pair. The MTH-Net introduces a multi-class feature generation (MFG) module to produce diverse features tailored for spatiotemporal modeling. Its core Spatiotemporal Difference Perception Network (SDPN) effectively integrates Mamba for efficient long-sequence temporal modeling and Transformer for fine-grained spatial dependency capture, leveraging a broadcasting mechanism for complementary fusion. A feature-sharing strategy is employed to reduce the computational overhead in multi-task learning. Extensive experiments on the LEVIR-CDC, WHU-CDC, and LEVIR-MCI datasets demonstrate that the MTH-Net achieves a state-of-the-art performance in change captioning, validating the effectiveness of our hybrid design and feature-sharing mechanism. Full article

Change detection in high-resolution remote sensing imagery is vital for applications such as urban expansion monitoring, land-use analysis, and disaster assessment. However, existing methods often underutilize the differential features of bi-temporal images and struggle with complex backgrounds, illumination variations, and pseudo-changes, which hinder accurate identification of true changes. To address these challenges, this paper proposes a Siamese change detection network that integrates an adaptive scanning state-space model with frequency-domain enhancement. The backbone is constructed using Visual State Space (VSS) Blocks, and a Cross-Spatial Guidance Attention (CSGA) module is designed to explicitly guide cross-temporal feature alignment, thereby enhancing the reliability of differential feature representation. Furthermore, a Frequency-guided Adaptive Difference Module (FADM) is developed to apply adaptive low-pass filtering, effectively suppressing textures, noise, illumination variations, and sensor discrepancies while reinforcing spatial-domain differences to emphasize true changes. Finally, a Dual-Stage Multi-Scale Residual Integrator (DS-MRI) is introduced, incorporating both VSS Blocks and the newly designed Attention-Guided State Space (AGSS) Blocks. Unlike fixed scanning mechanisms, AGSS dynamically generates scanning sequences guided by CSGA, enabling a task-adaptive and context-aware decoding strategy. Extensive experiments on three public datasets (LEVIR-CD, WHU-CD, and SYSU-CD) demonstrate that the proposed method surpasses mainstream approaches in both accuracy and efficiency, exhibiting superior robustness under complex backgrounds and in weak-change scenarios. Full article