Multi-object tracking (MOT) in satellite videos is a challenging task due to the small size and blurry features of objects, which often lead to intermittent detection and tracking instability. Many existing object detection and tracking models often struggle with these issues, as they are not designed to effectively handle the unique characteristics of satellite videos. To address these challenges, we propose LocaLock, a joint detection and tracking framework for MOT that incorporates feature matching concepts from single object tracking (SOT) to enhance tracking stability and reduce intermittent tracking results. Specifically, LocaLock utilizes an anchor-free detection backbone for efficiency and employs a local cost volume (LCV) module to perform precise feature matching in the local area. This provides valuable object priors to the detection head, enabling the model to “lock” onto objects with greater accuracy and mitigate the instability associated with small object detection. Additionally, the local computation within the LCV module ensures low computational complexity and memory usage. Furthermore, LocaLock incorporates a novel motion flow (MoF) module to accumulate and exploit temporal information, further enhancing feature robustness and consistency across frames. Rigorous evaluations on the VISO dataset demonstrate the superior performance of LocaLock, surpassing existing methods in tracking accuracy and precision within the demanding satellite video analysis domain. Notably, LocaLock achieved state-of-the-art performance on the VISO benchmark, achieving a multi-object tracking accuracy (MOTA) of 62.6 while ensuring fast running speed.

As a key task in remote sensing analysis, semantic segmentation of remote sensing images (RSI) underpins many practical applications. Despite its importance, obtaining dense pixel-wise annotations remains labor-intensive and time-consuming. Unsupervised domain adaptation (UDA) offers a promising solution by utilizing knowledge from labeled source domains for unlabeled target domains, yet its effectiveness is often compromised by significant distribution shifts arising from variations in imaging conditions. To address this challenge, we propose a depth-aware adaptation network (DAAN), a novel two-branch network that explicitly leverages complementary depth information from a digital surface model (DSM) to enhance cross-domain remote sensing segmentation. Unlike conventional UDA methods that primarily focus on semantic features, DAAN incorporates depth data to build a more generalized feature space. This network introduces three key components: an adaptive feature aggregator (AFA) for progressive semantic-depth feature fusion, a gated prediction selection unit (GPSU) that selectively integrates predictions to mitigate the impact of noisy depth measurements, and misalignment-focused residual refinement (MFRR) module that emphasizes poorly aligned target regions during training. Experiments on the ISPRS and GAMUS datasets demonstrate the effectiveness of the proposed method. In particular, DAAN achieves an mIoU of 50.53% and an F1 score of 65.75% for cross-domain segmentation on ISPRS to GAMUS, outperforming models without depth information by 9.17% and 8.99%, respectively. These results demonstrate the advantage of integrating auxiliary geometric information to improve model generalization on unlabeled remote sensing datasets, contributing to higher mapping accuracy, more reliable automated analysis, and enhanced decision-making support.

Traditional Multiple Object Tracking (MOT) methods in satellite videos mostly follow the Detection-Based Tracking (DBT) framework. However, the DBT framework assumes that all objects are correctly recognized and localized by the detector. In practice, the low resolution of satellite videos, small objects, and complex backgrounds inevitably leads to a decline in detector performance. To alleviate the impact of detector degradation on track, we propose Coarse-Fine Tracker, a framework that integrates the MOT framework with the Tracking Any Point (TAP) method CoTracker for the first time, leveraging TAP’s persistent point correspondence modeling to compensate for detector failures. In our Coarse-Fine Tracker, we divide the satellite video into sub-videos. For one sub-video, we first use ByteTrack to track the outputs of the detector, referred to as coarse tracking, which involves the Kalman filter and box-level motion features. Given the small size of objects in satellite videos, we treat each object as a point to be tracked. We then use CoTracker to track the center point of each object, referred to as fine tracking, by calculating the appearance feature similarity between each point and its neighboring points. Finally, the Consensus Fusion Strategy eliminates mismatched detections in coarse tracking results by checking their geometric consistency against fine tracking results and recovers missed objects via linear interpolation or linear fitting. This method is validated on the VISO and SAT-MTB datasets. Experimental results in VISO show that the tracker achieves a multi-object tracking accuracy (MOTA) of 66.9, a multi-object tracking precision (MOTP) of 64.1, and an IDF1 score of 77.8, surpassing the detector-only baseline by 11.1% in MOTA while reducing ID switches by 139. Comparative experiments with ByteTrack demonstrate the robustness of our tracking method when the performance of the detector deteriorates.