Search Results (3,564)

Cloud detection is a fundamental task in atmospheric science and satellite remote sensing. While numerous algorithms utilizing multiple visible and infrared channels have been developed, the absence of visible light at night forces most current methods to rely on multi-channel thermal infrared (TIR) observations. Consequently, detection accuracy is significantly reduced due to the minimal thermal contrast between low clouds and the ground. Furthermore, distinguishing clouds under strictly moonless conditions remains a critical challenge. Leveraging the low-light observation capability of the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB), this study proposes a single-channel cloud detection algorithm. Based on the physical scattering of ground-based artificial lights by clouds, the algorithm integrates a feature-engineering layer with a Random Forest machine learning model. This moonlight-independent approach can rapidly determine cloudy conditions, offering a novel method for high-precision nighttime cloud detection. Validation experiments using a single fixed radar site in Longmen, China, with 97 rigorously synchronized satellite-radar sample pairs, demonstrate that the proposed algorithm achieves an overall accuracy of 86.6% (95% CI: 78.4–92.0%) against millimeter-wave cloud radar observations. While strictly reliant on stable artificial ground lights—making it primarily applicable to urban and artificially lit regions—this method provides a valuable supplementary tool for nighttime monitoring. Full article

To address the issues of insufficient single-modality robustness and limited multi-scale object detection accuracy in remote sensing image detection (RSID) in complex environments, this paper proposes a multimodal RSID network named VI-MSFFN. The model adopts a symmetric parallel dual-branch architecture to achieve independent extraction and collaborative modeling of visible and infrared modal features. A cross-modal multi-scale sparse cross-attention fusion module is proposed and applied to the P4 and P5 feature layers, and a high-low-level feature collaborative cross-modal fusion strategy was constructed to achieve efficient and robust cross-modal feature fusion while enhancing multi-scale object modeling capability and suppressing feature redundancy and noise. Additionally, a progressive feature interaction and fusion architecture was designed to combine spatial and frequency domain information to strengthen deep object representation. The experimental results on the VEDAI and Drone Vehicle datasets demonstrate that VI-MSFFN achieves state-of-the-art (SOTA) performance in detection accuracy, robustness, and generalization ability. The proposed method effectively solves the detection challenges of RSID and has significant application value in the field of multi-modal RSID. Full article

Rotated ship detection in complex synthetic aperture radar (SAR) scenes remains a critical yet challenging task for maritime remote sensing applications. Existing methods are plagued by three core bottlenecks: inconsistent directional responses across multi-scale features, unstable rotation angle regression, and non-uniform supervision quality of positive samples during training, which collectively lead to elevated false alarms, missed detections, and severe localization degradation, especially under high IoU thresholds in complex inshore environments. To address these challenges, we propose CORE-Net, a collaborative optimization framework integrating three dedicated modules in the forward detection stage: a Rotation-Consistent Feature Pyramid (RCFP) to alleviate cross-scale directional mismatch, a Progressive Cascade Rotation Head (PCR Head) to improve progressive angle prediction stability, and an Orientation-Aware Regression Enhancement Unit (OAREU) to strengthen directional geometric representation in regression features, alongside an Uncertainty-Aware Sample Reliability Steering (UARS) module for training-stage optimization to softly downweight the regression contribution of positive samples with high classification confidence but low geometric consistency. Extensive experiments on three public SAR ship detection datasets (RSDD-SAR, SSDD+, and RSAR) demonstrate that the proposed method consistently improves AP_50:95 while maintaining high Recall and Precision, validating that joint optimization of feature representation, rotated regression, and sample reliability is an effective strategy to enhance both the robustness and fine-grained localization capability of rotated ship detection in complex SAR scenes. In addition, large-scene inference experiments on uncropped Sentinel-1 and RSDD-SAR images further demonstrate that CORE-Net can be extended from patch-based evaluation to high-resolution SAR scene interpretation using a sliding-window inference strategy. Full article

Hyperspectral images (HSIs) and Light Detection and Ranging (LiDAR) data offer complementary spectral and spatial information and are extensively applied to land cover classification. Nevertheless, current fusion–classification approaches frequently suffer from cross-modal feature entanglement and insufficient exploitation of LiDAR physical priors, particularly the Digital Surface Model (DSM), which limits the interpretability of learned features and restricts classification accuracy. To address these issues, this study presents a Physics-Guided Adaptive Decoupling and Collaborative Enhancement Network (ADCE-Net) that embeds explicit geometric guidance into multimodal feature learning. In ADCE-Net, the DSM serves as an explicit geometric conditioning signal to guide feature decoupling, decomposing input representations into modality-shared semantic features (SSF) and modality-specific discriminative features (MSF), thereby mitigating cross-modal interference at an early stage. Based on this decomposition, an adaptive collaborative enhancement mechanism is designed using bidirectional cross-attention and dynamic gating to achieve context-aware mutual refinement between SSF and MSF, facilitating more effective utilization of cross-modal complementary information. Furthermore, a multi-level collaborative classification architecture is constructed to integrate multi-scale contextual representations, enhancing spatial consistency and boundary delineation. Extensive experiments on three benchmark datasets—Trento, Houston 2013, and Muufl Gulfport—demonstrate that ADCE-Net achieves overall accuracies of 99.69%, 97.37%, and 94.90%, respectively, surpassing multiple representative methods including support vector machines, 3D convolutional neural networks, transformer-based models, and recurrent neural networks. Noticeable improvements are also achieved for minority classes and classes with highly similar spectral signatures. The DSM-driven physics guidance boosts both classification performance and feature interpretability, providing a reliable and explainable paradigm for multimodal remote sensing classification. Full article

The detection technology of crop diseases and pests is transitioning from single sensor monitoring to intelligent perception and multimodal fusion. This paper follows the PRISMA 2020 standard and systematically reviews the relevant core literature. This paper systematically summarizes the development history of spectral sensing technology and analyzes the physical mechanisms of hyperspectral and multispectral imaging in early identification of crop diseases. The focus is on the architectural evolution of deep learning models, including lightweight convolutional neural networks (CNNs), vision transformers (ViTs) with long-range dependency modeling capabilities, and the efficient computing state space model Mamba. In addition, the research progress of spatial spectral joint learning, heterogeneous data fusion, and vision-language models (VLMs) in improving system robustness and interpretability are introduced. By synthesizing the integrated applications of UAV remote sensing, Internet of Things (IoT) edge computing and intelligent robots in staple and cash crops, this paper summarizes the implementation of the integrated system of perception, decision-making and execution. To address the issues of insufficient cross-domain generalization ability and uneven allocation of computing resources in existing models, this paper provides perspectives on the future development of agricultural artificial intelligence (AI) towards foundation model-driven, edge-intelligent collaboration, and green sustainable direction, which can provide theoretical reference for engineering applications in the field of intelligent plant protection. Full article

Small, dispersed, and vegetated creeping landslides in hilly areas of eastern China hinder traditional remote sensing and detection. To address this, this study takes Yixian County (Anhui Province) as a representative area. Based on high-resolution GF-2 satellite images, three deep learning models embedded with the Squeeze-and-Excitation (SE) attention mechanism (ResNet18-SE, VGG13-SE, UNet-SE) were developed and compared with the original UNet model. Combined with field investigation, landslide mapping and accuracy assessment were conducted to evaluate the feature extraction capabilities of four models. The results indicate that the UNet-SE model achieved optimal performance (Precision: 0.911, Recall: 0.685, F1-score: 0.782, Kappa: 0.730, IoU: 0.643). Its F1-score exceeds ResNet18-SE, VGG13-SE, and the original UNet by 8%, 3%, and 5%, respectively, proving superior regional adaptability and generalization performance. Additional verification on creeping landslides in Kecun Town (Yixian County) and post-earthquake landslides in Lushan County (Sichuan Province) further confirms the reliability of the UNet-SE model. Furthermore, Frequency Ratio (FR), Random Forest (RF), and SHapley Additive exPlanations (SHAP) were adopted to reveal the correlation between landslide occurrence and seven geological-environmental factors. Landslides are most susceptible to develop at elevations of 400–500 m, NDVI values of 0.4–0.5, slopes below 10°, east and northeast aspects, 300–500 m away from rivers, 500–1000 m away from faults, and areas covered by soft sedimentary lithology. Distance from faults, distance from rivers, and elevation are identified as the three favorable conditional factors. In conclusion, the proposed landslide detection framework can provide reliable spatial data and technical references for regional geological hazard prevention, ecological conservation and sustainable development in hilly areas. Full article

Early wildfire monitoring in power transmission corridors requires reliable detection of weak fire and smoke cues under complex field conditions and strict edge-computing constraints. To address these issues, this paper proposes an edge-deployable RGB–thermal framework based on visible and thermal infrared (TIR) imaging for unmanned aerial vehicle (UAV)-based corridor monitoring, including a spatial detector, YOLO-MMSC, and a temporal-enhanced version, YOLO-MMSC-T. The study also establishes a self-collected corridor-oriented RGB–thermal (RGB–T) dataset to complement public wildfire data. Unlike existing RGB–thermal wildfire datasets that mainly focus on forest or wildland fire scenes, the proposed dataset is specifically organized for complex-background power transmission-corridor monitoring, including continuous UAV sequences, nighttime conditions, smoke/vegetation occlusion, long-range small targets, and hard-negative interference. To the best of our knowledge, this is the first self-collected RGB–thermal wildfire dataset designed for this specific application scenario. The framework integrates a mobile inverted bottleneck convolution (MBConv) lightweight backbone, a Shallow Detail Fusion Module (SDFM) for shallow cross-modal alignment and denoising, a Content-Guided Attention (CGA) module for adaptive fusion, and normalized Wasserstein distance (NWD)-based box regression for long-range small-target localization. Experiments on public and self-collected datasets show that YOLO-MMSC achieves 94.6% mAP@0.5, 95.0% precision, and 93.9% recall while running at 60 FPS on Jetson Orin NX. With temporal fine-tuning, YOLO-MMSC-T reaches a continuous detection rate (CDR) of 95.6% with a jitter index of $2.8\times {10}^{-3}$. Field experiments using a DJI Matrice 4T further indicate a practical operating altitude of 120–180 m. These results support lightweight RGB–thermal remote sensing for real-time wildfire monitoring in complex transmission-corridor environments. Full article

In recent years, salient object detection in optical remote sensing images (ORSI-SOD) has garnered increasing research attention. However, in practical applications, issues such as blurred target edges under low-contrast and complex background interference continue to restrict the accuracy and robustness of detection. To address these problems, this paper proposes the Phase Congruency-Guided Cross-Scale Contextual Fusion Network (PCFNet). Specifically, we design a novel Phase Congruency Enhanced Module (PCE) to solve the problem of low-contrast between targets and backgrounds. It acquire phase features via Fourier decomposition and employs them to generate a weighting map to modulate the shallow features via element-wise multiplication, thereby highlighting structurally significant regions. Meanwhile, we adopt a tailored loss weighting mechanism to weight phase congruency learning for better PCE adaptation. To address complex background interference, we design a novel Dynamic Residual Fusion (DRF) Module. It leverages dynamic spatial attention to generate sample-specific kernels that perform convolution to spatially weight features and uses consecutive residual connection, thereby refining multi-scale features to accurately capture effective targets under complex background interference. Experiments on ORSSD, EORSSD, and ORSI4199 benchmarks demonstrate that PCFNet achieves nine best performances and three second-best performances across the twelve core evaluation metrics, outperforming 23 state-of-the-art methods. Notably, the $F_{\beta }$ score is 1.16% higher than HFCNet on ORSSD and 0.85% higher than MCPNet on EORSSD. Full article

High-precision cloud detection is essential for remote sensing applications such as agricultural monitoring and disaster response. However, thin clouds severely limit detection accuracy. The difficulty lies in their semi-transparent nature, which causes their reflected signals to couple with the reflectance of various underlying surfaces. This coupling leads to inconsistent cloud signatures and significant intra-class variability. To address this, we propose a Class Center Residual Attention Network (CCRANet), a radiative transfer theory-inspired framework that employs a class center approach to extract the intrinsic reflective characteristics of thin clouds. Specifically, the core of the network is the Class Center Attention (CCA) module, which extracts invariant intrinsic features of thin clouds, supplemented by the Class Center Residual (CCR) module to eliminate surface-induced interference. Experiments on three public datasets (Landsat-8, CSWV, and CloudS26) show that CCRANet achieves a mean Intersection over Union (mIoU) of 85.93% on the Landsat-8 dataset, outperforming the classic DeeplabV3+ baseline by 10.23 percentage points. In particular, it achieves 22.58 percentage point improvement in thin cloud IoU over DeeplabV3+ in snow/ice scenarios, significantly reducing false positive detections caused by surface spectral similarity. Full article

With the rapid development of UAV and satellite remote sensing, in combination with deep learning, high-efficiency monitoring of pine wilt disease (PWD) for forest health management is now feasible. Accurate detection has not yet been realised. The sensing platforms have different ranges of space, observation areas and imaging orientations. At the same time, the target groups for PWD often have weak phenotypic features, are easily affected by a complex forest background, and show irregular data distributions at different stages of the disease. The above factors are limits to the performance of traditional methods based only on general visual features. To address the problems mentioned above, we propose the cross-platform semantic-consistent and phenotype-adaptive detection network SCPA-Net for high-precision PWD detection in both UAV and satellite images. First, we construct a cross-platform multimodal framework to integrate remote sensing images and disease-related text descriptions. The above design adds semantic prior knowledge to expand the model’s capacity for high-level phenotypic attribute extraction without direct observation. Second, to reduce the semantic gap caused by the different platforms, improve the semantic consistency of UAV and satellite images, strengthen discriminative feature channels and salient regions, and address cross-platform misalignment. Third, since the targets are often associated with complex forest environments, target-context relational modeling is enhanced and irrelevant interference is suppressed to reduce the impact of non-causal attributes. As pine wilt disease symptoms gradually progress from mild to severe (e.g., crown discoloration, texture variation, and wilting severity), differences among disease stages may lead to learning imbalance and knowledge forgetting; therefore, a staged adaptation strategy has been proposed. First, the model learns from relatively easy examples. Subsequently, it progressively learns from more difficult examples to enhance generalization performance. Experiments have been conducted on a self-built cross-platform dataset, a satellite dataset, the PDT public dataset, and the Roboflow dataset, and the proposed method has achieved better detection accuracy and generalization. The framework can address the problem of PWD detection in challenging-to-process forestry remote sensing data reasonably well. Full article

Seismically triggered landslides represent a major secondary hazard of earthquakes, often causing widespread damage over large areas. Rapid and reliable mapping of such phenomena is therefore essential, particularly in emergency contexts. While numerous studies have addressed landslide detection in vegetated regions using optical remote sensing, arid and desert environments remain relatively underexplored due to the limited spectral contrast between stable and failed slopes. In this study, we evaluate the potential of an automatic pixel-based method for the rapid detection of seismic landslides in arid settings, using high-resolution optical imagery. The analysis focuses on the Mw 5.5 earthquake that struck the Northern Red Sea Region of Eritrea on 26 December 2022. A detailed inventory of 1393 coseismic landslides was manually mapped from pre- and post-event PlanetScope multispectral images and used both for geomorphological and macroseismic analyses and as training data for a threshold-based classification approach. Landslide detection was based on changes in the Redness Soil Index (RSI) and its differential (ΔRSI), combined with a One-Class Asymmetric Robust Gaussian classifier. Results show a good capability to delineate landslide-affected areas, although commission errors remain significant. Despite these limitations, the proposed approach, still in need of a more trained implementation in the future, proves its potential effectiveness for rapid mapping purposes, owing to its simplicity and minimal computational requirements. These results open the possibility to implement a fully automatic methodology in the future, when more landslides will be mapped and a model trained on different and normalized datasets will be implemented. The results demonstrate that pixel-based optical methods, particularly those relying on red-band spectral changes, represent a valuable tool for the preliminary assessment of earthquake-induced landslides in arid environments and may support emergency response and first-order hazard evaluation. Full article

Remote sensing change detection (RSCD) identifies land cover variations by comparing bi-temporal images. However, conventional methods relying solely on RGB domain information often fail to distinguish changed objects from visually similar backgrounds, especially in complex scenarios. To overcome this limitation, we propose a frequency-aware refinement network (FARNet) that follows a coarse-to-fine strategy. In the first stage, we design a frequency-aware module (FAM) that learns frequency domain information to identify the blurred boundaries of changed objects that resemble the background, enabling coarse localization of potential change regions. In the second stage, recognizing that high-resolution RGB domain details provide richer spatial information than frequency-domain features, we design a refinement fusion module (RFM) that leverages these RGB details to correct and refine segmentation boundaries, ensuring precise detection. Finally, edge loss is applied to preserve high-frequency details, enhancing the precision of change detection. Extensive experiments on benchmark datasets demonstrate that FARNet significantly outperforms existing methods, achieving superior accuracy and robustness in complex change detection scenarios. Full article

Timely landslide detection and rapid qualitative assessment are fundamental to effective warning systems, hazard management, and risk mitigation. Yet, current practices that rely on on-site surveys and manual expert assessment remain risky, costly, and time-consuming. These limitations result in substantial delays between the event and the availability of actionable information. This study proposes a hybrid, multi-model framework that fuses RGB remote-sensing imagery with geospatial layers to enable timely landslide detection and actionable reporting. The pipeline couples an enhanced SegFormer (denoted as SDF-SegFormer-B2) model for landslide localization, a feature extraction technique for per-slide geo-attribute computation, and a lightweight instruction-tuned LLM (Mistral-7B-Instruct-v0.3) for structured, expert-style reporting. Although a few previous studies have explored landslide captioning, to our knowledge this is the first framework designed to generate structured technical reports enriched with terrain-context interpretation and qualitative intervention-priority indicators. Experiments use 26,758 georeferenced RGB tiles (64 × 64) with 3 m of spatial resolution from PlanetScope satellite imagery over Emilia–Romagna, Italy, with 68,592 annotated landslide boxes collected after the May 2023 rainfall events (~200 mm in 48 h on 1–3 May; 200–250 mm in 48 h on 16–17 May). The proposed SDF-SegFormer-B2 segmentation model achieved a precision of 85.54%, recall of 72.31%, and an F1-score of 78.39% on the unseen test dataset. To evaluate the quality of the generated landslide reports, 100 images were selected for domain-expert assessment. Among these, 58% of the reports were rated as “Very Good,” 30% as “Good,” 8% as “Acceptable,” and 4% as “Poor.” When considering only reports with complete and accurate inputs, 81.48% were rated “Very Good,” and 96.30% were rated either “Good” or “Very Good.” By integrating complementary models and modalities, the proposed approach automates localization-to-reporting and enables the generation of terrain-aware landslide summaries that may support preliminary decision-making and rapid post-disaster screening. Full article

Remote sensing change detection is crucial for urban expansion monitoring and ecological assessment. Recently, methods based on Convolutional Neural Networks (CNNs) and Transformers have advanced significantly. However, state-of-the-art models relying primarily on pure spatial-domain modeling and absolute feature differences struggle to balance global semantics with high-frequency boundary details. This paradigm loses physical change directionality and amplifies pseudo-change noise in complex backgrounds. To overcome this, we propose a Frequency-Aware Cross-Layer Change Detection Network (FACDNet) that leverages frequency-spatial synergy to enhance feature discriminability. Specifically, a Wavelet Interaction Block (WIB) decouples bitemporal features using Haar wavelets, employing heterogeneous attention to targetedly reinforce macroscopic semantics and edge textures. Furthermore, to mitigate noise in shallow features, a Cross-Layer Frequency Context Aggregator (CLFCA) injects deep global semantics top-down, purifying multi-scale spatial gating signals. Finally, a Context-guided Difference Fusion Module (CDFM) extracts direction-aware bidirectional difference features, utilizing the purified gating to accurately suppress pseudo-changes. Extensive experiments on the LEVIR-CD and highly challenging SHCD datasets demonstrate FACDNet’s remarkable robustness. It achieves change-class F1-scores of 92.04% and 83.64%, and Intersection over Union (IoU) scores of 85.26% and 71.89%, respectively, achieving highly competitive performance compared with existing mainstream methods. Full article

Civil infrastructure plays a critical role in ensuring societal safety and economic development. However, structural damages such as cracks inevitably occur during long-term service. Traditional manual inspection methods are insufficient to meet the demands of large-scale and routine monitoring. Unmanned Aerial Vehicles (UAV) remote sensing has become an important approach for Structural Health Monitoring (SHM), owing to its high spatial resolution imaging capability and superior operational flexibility. Nevertheless, existing studies focus on optimizing individual algorithms, lacking a systematic analysis oriented toward multi-scenario engineering applications. Therefore, we present a comprehensive review of UAV-based crack detection techniques for infrastructure using remote sensing imagery. First, publicly available datasets, UAV platforms, and evaluation metrics are systematically summarized. Then a multi-level visual analysis framework for UAV inspection is established. The framework categorizes existing methodologies into five levels: image-level classification, object-level detection, pixel-level segmentation, geometric quantification, and three-dimensional (3D) reconstruction, followed by a systematic evaluation of representative methods. Furthermore, the applicability of different methods across diverse scenarios, including bridges, pavements, dams, building facades and wind turbine blades, is systematically explored. Finally, the key challenges and future research directions are discussed. This review aims to provide a systematic theoretical foundation and methodological reference for advancing UAV-based infrastructure crack inspection from algorithm development toward practical multi-scenario engineering applications. Full article