Building change detection is a prominent topic in remote sensing applications. Scholars have proposed a variety of fully-convolutional-network-based change detection methods for high-resolution remote sensing images, achieving impressive results on several building datasets. However, existing methods cannot solve the problem of pseudo-changes caused by factors such as “same object with different spectrums” and “different objects with same spectrums” in high-resolution remote sensing images because their networks are constructed using simple similarity measures. To increase the ability of the model to resist pseudo-changes and improve detection accuracy, we propose an improved method based on fully convolutional network, called multitask difference-enhanced Siamese network (MDESNet) for building change detection in high-resolution remote sensing images. We improved its feature extraction ability by adding semantic constraints and effectively utilized features while improving its recognition performance. Furthermore, we proposed a similarity measure combining concatenation and difference, called the feature difference enhancement (FDE) module, and designed comparative experiments to demonstrate its effectiveness in resisting pseudo-changes. Using the building change detection dataset (BCDD), we demonstrate that our method outperforms other state-of-the-art change detection methods, achieving the highest F1-score (0.9124) and OA (0.9874), indicating its advantages for high-resolution remote sensing image building change detection tasks.

Dark ships, vessels deliberately disabling their AIS signals, constitute a grave maritime safety hazard, with detection efforts hindered by issues like over-reliance on AIS, inadequate surveillance coverage, and significant mismatch rates. This paper proposes an improved multi-feature association method that integrates satellite remote sensing and AIS data, with a focus on oriented bounding box course estimation, to improve the detection of dark ships and enhance maritime surveillance. Firstly, the oriented bounding box object detection model (YOLOv11n-OBB) is trained to break through the limitations of horizontal bounding box orientation representation. Secondly, by integrating position, dimensions (length and width), and course characteristics, we devise a joint cost function to evaluate the combined significance of multiple features. Subsequently, an advanced JVC global optimization algorithm is employed to ensure high-precision association in dense scenes. Finally, by integrating data from Gaofen-6 (optical) and Gaofen-3B (SAR) satellites, a day-and-night collaborative monitoring framework is constructed to address the blind spots of single-sensor monitoring during night-time or adverse weather conditions. Our results indicate that the detection model demonstrates a high average precision (AP50) of 0.986 on the optical dataset and 0.903 on the SAR dataset. The association accuracy of the multi-feature association algorithm is 91.74% in optical image and AIS data matching, and 91.33% in SAR image and AIS data matching. The association rate reaches 96.03% (optical) and 74.24% (SAR), respectively. This study provides an efficient technical tool for maritime safety regulation through multi-source data fusion and algorithm innovation.

The complexity of surface characteristics in rural areas poses challenges for accurate extraction of built-up areas from remote sensing images. The Artificial Surface Index (ASI) emerged as a novel and accurate built-up land index. However, the absence of short-wave infrared (SWIR) bands in most high-resolution (HR) images restricts the application of index-based methods in rural built-up land extraction. This paper presents a rapid extraction method for high-resolution built-up land in rural areas based on ASI. Through the downscaling techniques of random forest (RF) regression, high-resolution SWIR bands were generated. They were then combined with visible and near-infrared (VNIR) bands to compute ASI on GaoFen-2 (GF-2) images (called ${\mathrm{A}\mathrm{S}\mathrm{I}}_{\mathrm{G}\mathrm{F}}$). Furthermore, a red roof index (RRI) was designed to reduce the probability of misclassifying built-up land with bare soil. The results demonstrated that SWIR downscaling effectively compensates for multispectral information absence in HR imagery and expands the applicability of index-based methods to HR remote sensing data. Compared with five other indices (UI, BFLEI, NDBI, BCI, and PISI), the combination of ASI and RRI achieved the optimal performance in built-up land enhancement and bare land suppression, particularly showcasing superior performance in rural built-up land extraction.

Maritime unmanned aerial vehicle (UAV) ship detection faces challenges including variations in ship pose and appearance under multiple viewpoints, occlusion and confusion in dense scenes, complex backgrounds, and the scarcity of ship datasets from UAV tilted perspectives. To overcome these obstacles, this study introduces a high-quality dataset named Marship-OBB9, comprising 11,268 drone-captured images and 18,632 instances spanning nine typical ship categories. The dataset systematically reflects the characteristics of maritime scenes under diverse scales, viewpoints, and environmental conditions. Based upon this dataset, we propose a novel detection network named YOLO11-UAVShip. First, an oriented bounding box detection mechanism is incorporated to precisely fit ship contours and reduce background interference. Second, a newly designed CK_DCNv4 module, integrating deformable convolution v4 (DCNv4) and a C3k2 backbone structure, is developed to enhance geometric feature extraction under aerial oblique view. Additionally, for ships with large aspect ratios, SGKLD effectively addresses the localization challenges in dense environments, achieving robust position regression. Comprehensive experimental evaluation demonstrates that the proposed method yields a 2.1% improvement in mAP@0.5 and a 2.3% increase in recall relative to baseline models on the Marship-OBB9 dataset. While maintaining real-time inference speed, our approach greatly enhances detection accuracy and robustness. This work provides a practical and deployable solution for intelligent ship detection in UAV imagery.

The relocation of residents from high-risk areas is a critical measure to address safety and development issues in the floodplain regions of Henan Province in China. Whether the old villages can be reclaimed as farmland after demolition concerns Henan Province’s ability to maintain its farmland red line. This paper integrated multiple remote sensing indices and proposed a remote sensing identification method for monitoring the progress status of village relocation and reclamation that adapted to data characteristics and application scenarios. Firstly, it addressed the issue of missing target bands in GF-2 (GaoFen-2) by employing a band downscaling method; secondly, it combined building and vegetation indices to identify changes in land cover in the old villages within the floodplain, analyzing the implementation effects of the relocation and reclamation policies. Results showed that using a Random Forest regression model to generate a 4 m resolution shortwave infrared band not only retains the original target band information of Landsat-8 but also enhances the spatial detail of the images. Based on the optimal thresholds of multiple remote sensing indices, combined with human footprint data and POI (Points of Interest) identified village boundaries, the overall accuracy of identifying the progress status of resident relocation and reclamation reached 93.5%. In the floodplain region of Henan, the implementation effect of resident relocation was relatively good, with an old village demolition rate of 77%, yet the farmland reclamation rate was only 23%, indicating significant challenges in land conversion, lagging well behind the pilot program schedule requirements. Overall, this study made two primary contributions. First, to distinguish between rural construction and bare soil, thereby improving the accuracy of construction land extraction, an Enhanced Artifical Surface Index (EASI) was proposed. Second, the monitoring results of land use changes were transformed from pixel-level to village-level, and this framework can be extended to other specific land use change monitoring scenarios, demonstrating broad application potential.

Unmanned aerial vehicle (UAV) video and Automatic Identification System (AIS) data provide high-frequency visual observations and low-frequency identity/location messages, respectively. However, these two types of sensor data differ in spatiotemporal dimensions and pose engineering challenges for association and matching. This paper proposes a scene-adaptive trajectory association framework for airborne maritime perception. First, visual trajectories are mapped into a geospatial coordinate system using camera, gimbal, and flight platform metadata. Second, sparse AIS position sequences are filtered with physical constraints and locally interpolated to reconstruct continuous trajectories. Finally, based on target motion significance and local candidate complexity, the framework dynamically switches between Instantaneous Synchronization Multi-Feature Association (ISMA) and Spatiotemporal Constraint Weighted Dynamic Time Warping (STC-WDTW), leveraging kinematic features to deeply optimize the matching strategy, significantly enhancing robustness in trajectory association. This study collects data from the Port of Tianjin and adjacent coastal waters and evaluates the proposed method based on this dataset. Experimental results demonstrate that the proposed method achieves 94.34% IDP, 92.02% IDR, 93.17% IDF1, and 86.50% MOFA. The proposed method can be applied to marine dynamic monitoring systems, providing technical support and practical value for vessel traffic management in complex maritime areas and multi-source data fusion.