In order to improve the accuracy of localization of projectile impact point, detection and localization methods of projectile impact point based on remote sensing imagery are studied in this paper. We constructed a labeled dataset named SFP about smoke, fire, and impact point containing 6700 images. We designed a two-stage segmentation algorithm based on prior knowledge. Our algorithm first segments the region containing smoke and fire, which is treated as the Region of Interest (ROI). Within the ROI, our algorithm then segments impact point. Our algorithm is compared with the direct segmentation algorithm on SFP. The precision, recall, and mAP of our algorithm are 0.693, 0.654, and 0.639. And those of the direct segmentation algorithm are 0.263, 0.175, and 0.041. The performance of our algorithm is much higher, which indicates that our algorithm can accomplish the task of detecting and localizing the impact points based on remote sensing imagery better.

Satellite video multi-object tracking holds significant importance in national defense security, emergency rescue, urban planning, and various other applications. This task is particularly challenging due to the complex background of remote sensing images and the dynamic nature of spatiotemporal processes. Currently, satellite video object tracking faces issues like erroneous detection and tracking, especially in cases of low contrast and missed detection and tracking for small objects. To address these challenges, this study introduces a method for multi-object tracking in satellite videos that considers both global information and local features of objects. The proposed method also incorporates weak feature enhancement techniques, specifically targeting issues such as low contrast. Extensive experimental verification on Jilin-1 satellite video data has been conducted, yielding excellent results and demonstrating the effectiveness of the proposed approach.

Matching multimodal remote sensing images (RSIs) remains an ongoing challenge due to the significant nonlinear radiometric differences and geometric distortions, resulting in matches exhibiting one-to-many matches or mismatches. To tackle this challenge, we propose a novel approach for multimodal RSI matching called modality-independent consistency matching (MICM), which leverages the capabilities of deep convolutional neural networks and the transformer attention mechanism to improve the matching performance. The proposed MICM method consists of three key steps. First, a Unet-like feature extraction backbone network is employed to learn multiscale invariant features from multimodal RSIs, enabling the extraction of rich and evenly distributed feature keypoints. Second, a hybrid approach combining local learning features with the transformer attention mechanism is introduced to aggregate learning features, facilitating both detailed capture and long-range modeling to enhance the representation ability of the features. Third, a feature consistency correlation strategy is adopted to maximize the number of correct corresponding feature points, ensuring reliable matching performance. The performance of the proposed method has been extensively evaluated on both the same scene and different scene multimodal RSIs, which are captured from various imaging modes, wavebands, and platforms. The results show the superior matching performance of the proposed MICM method compared to commonly used and state-of-the-art handcrafted- and learning-based methods when evaluated on both the same scene and different scene datasets. The proposed method serves as a valuable reference for addressing common challenges in multimodal RSI matching.

Existing change detection (CD) research mostly focuses on pixel-level dense prediction, while object detection (OD) for entire damaged/changed buildings is rare. Object-level detection datasets for damaged/changed buildings are also lacking. This article proposes an object-oriented damaged/changed building CD model, OoCDNet, along with five global-scale OD datasets for damaged/changed buildings. OoCDNet bridges and integrates the dual tasks of CD and OD, driven by the task of locating damaged/changed buildings. By modeling the changes in buildings at the object level between bitemporal images, it achieves rapid identification of target buildings. OoCDNet consists of four parts, the two paths of the dual-path feature extraction module (DouBackbone) are responsible for extracting the base features of the image, the bidirectional pyramid feature aggregation module (AggNeck) models the change information of the features aggregated at the end, the cross-informative self-attentive short-circuit enhancement module enhances the features from the DouBackbone with highly efficient self-attentive information and supplements the enhancement information to the AggNeck, and the enhanced features with semantic and localization information are fed into the detection module for OD. The proposed OoEWEBD is a global-scale OD dataset for damaged buildings, containing 10377 image pairs, each sized$256\times 256$pixels. The remaining four datasets are created based on the WHU_CD, LEVIR-CD+, S2Looking, and xBD datasets, targeting buildings with general changes or those affected by disasters. Compared with state-of-the-art (SOTA) OD and CD methods, OoCDNet can quickly and effectively detect target buildings, achieving the highest accuracy and having a high application value. The code and datasets will be available athttps://github.com/Haiming-Z/OD-based-Change-Detection-mode-OoCDNet.

Multimodal remote sensing image (MRSI) high-precision matching faces significant challenges due to the large nonlinear distortions in radiation and geometry. To address this problem, we propose a novel MRSI matching method, multimodal remote sensing image matching with Delaunay triangulation constraint network (M2DT-Net), which takes advantage of deep convolutional neural networks (CNNs) and the Delaunay triangulation (DT) strategy. First, a fine-tuned CNN model is applied to learn invariant features with high robustness across MRSIs. Second, an adaptive feature descriptor distance constraint combined with the DEGENSAC refinement is employed to filter the obtained matches, and only the matches with high credibility are retained. Finally, a DT strategy is adopted to expand more underlying correct matches that are dropped inliers in the previous stage. The experimental results on seven types of MRSI pairs indicate that M2DT-Net is superior to seven state-of-the-art image matching algorithms in terms of model performance and efficiency, achieving a balance of robustness and time consumption. The image registration results further demonstrate the effectiveness of the proposed algorithm. Therefore, the proposed M2DT-Net method provides a reference for resolving common multimodal image matching problems.