Search

In computer vision, single object tracking in satellite video is a trending research topic with challenging problems. The high-resolution remote sensing camera captures spatiotemporal information from Earth, where tracking of interested targets is possible. Due to nadir view, single object tracking in satellite videos faces model drift problem due to limited features of tiny objects. Rotation of object, background clutter and long period occlusion of moving objects lead to model drift problem. This study proposes a kernelized correlation filter framework-based tracker using modified ResNet50 with spatial pyramid pooling as feature extractor which solve the short rotation problem and get the shape, textures and patterns features of small size object. To solve long period occlusion problem, this paper proposes an average of average based motion estimation method with Kalman filtering. The proposed method was implemented, tested and quantitative experiments were conducted on satellite video SatSOT dataset. From the obtained results, it is found that the proposed tracker produces better performance in comparison to state-of-the-art tracker methods available in literature in terms of success score, precision score and speed. [ABSTRACT FROM AUTHOR]

As a novel data source for earth observation, satellite video can provide large-scale temporal information for dynamic monitoring. However, the cloud occlusion prevents satellite video from continuous and seamless observation of the earth's surface. We propose the first satellite video cloud removal model RFE-VCR to approach this problem. In RFE-VCR, an efficient strategy of taking distant frames into training period is applied. A reference enhance block based on gated aggregation layers is proposed to explore the complementary information hidden in distant frames. A bidirectional local enhance block using deformable convolution is improved for feature refinement. Moreover, a decoupled temporal-spatial transformer is utilized for long-distance dependence modeling. Simulative and real experiments on Jilin-1 satellite videos demonstrate that our proposed network can achieve remarkable performance in video cloud removal task, as well as sensitive object hiding and high-reflection removal. More dynamic results of our experiments can be found at https://xyjin99.github.io/RFE-VCR/. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper studies moving object detection in satellite videos, which plays a significant role for large-scale video monitoring and dynamic analysis. Due to the tiny targets, complex background, and completely or partly occlusion, moving object detection accurately from each image frame is difficult and challenging. In order to tackle the issues, we propose an improved Visual Background Extractor algorithm (ViBe) with an improved Canny edge detection operator. First, the improved Canny operator based on iteration method is employed to find the optimal high and low thresholds and extract more edges information of satellite videos. Furthermore, the eight pixels are replaced with twenty-four pixels and the times of target threshold is introduced in ViBe detection to increase the selection range of backgrounds. The experiments are tested in SATSOT datasets, where the results verify that the proposed method is more robust in complex background with interference, turning and rapid motion. [ABSTRACT FROM AUTHOR]

Satellite video object tracking has become an emerging technology for dynamically observing the earth, providing the possibility for tracking moving objects in a short time. Deep learning methods such as CNN-based trackers and transformer-based trackers have been widely applied for single object tracking in natural videos. The target in natural videos is captured by ground sensors, whereas satellite sensors come from high altitudes of hundreds of kilometers or more, the trackers designed for natural videos may suffer the influence of complex background, especially small targets with weak features in view of remote sensing platforms. Furtherly, the confusion of visually similar objects with the target and the deformation of target in satellite videos can also lead to incorrect positioning. To address these problems, we proposed a target-aware bilateral CNN-Transformer network (TabCtNet). In TabCtNet, the bilateral CNN-Transformer architecture with the aggregation and interaction of local spatial information and global temporal context is designed to tackle the challenge of small target with weak features in complex and clutter background in satellite videos. To effectively reduce the impact of similar objects, the target-aware block-erasing strategy (TAS) is constructed to generate weakened heatmaps from the template target mask in a data-driven manner. Moreover, a pixel-wise refinement module with corner-based box estimation (PE) is designed to extract more fine-grained spatial information for more accurate box estimation and reduce the effect of target deformation. Experimental results show that TabCtNet quantitatively and qualitatively outperforms advanced single object tracking methods on two different satellite video datasets with four categories of targets from different scenarios. Furthermore, to investigate the generalizability of the TabCtNet framework, satellite videos sourced from different countries captured by various satellite platforms were used for evaluation, and the results reveal its robust performance across various scenarios.

Object tracking in satellite videos contains various small targets, such as cars and ships. However, since small targets always lack salient texture features and have low contrast to the background, it is difficult to detect targets and distinguish different target instances, which results in tracking failure. In this letter, we propose a Siamese multidimensional fusion and time-domain coding network (SMTN) with an efficient attention-based multidimensional information fusion (MDF) module and a time-domain information fusion (TDF) module. The MDF module fuses a multiscale template map and search map information to make the network focus on the target, thus improving the target discriminability. In the TDF module, the aggregated temporal information of previous frames is used to adjust the current frame response map through a local-sensing Metaformer module, which suppresses the response of similar interferences. Different from other temporal fusion methods in object tracking in satellite videos, the TDF enables video satellite trackers to be optimized end-to-end and improves the tracking success rate. Experiments demonstrate that the proposed method outperforms state-of-the-art trackers. [ABSTRACT FROM AUTHOR]

Satellite video is a novel data source for earth observation, which can be applied in multiple fields for dynamic monitoring. It is always equipped with high temporal resolution at the cost of low spatial resolution of tiny moving objects. Video super-resolution (VSR) is utilized to improve the spatial resolution of satellite video and obtain high spatial-temporal resolution data. However, most existing VSR methods mainly focus on the local interframe information during feature alignment, which lack the ability to model long-distance correspondence. In this letter, a novel two-branch alignment network with an efficient fusion module is proposed for satellite VSR. Both deformable convolution (DCN) and transformer-like attention are employed to fully explore the local and global information between frames. Furthermore, a fusion module is proposed to model the residuals between fusion features and compensate them for better fusion. Experiments on Jilin-1 satellite videos demonstrate that the proposed network can achieve comparable results to current state-of-the-art (SOTA) VSR methods with tiny parameters. [ABSTRACT FROM AUTHOR]

Using remote sensing video to monitor aircraft dynamics is significant for military applications, airport management, and aircraft rescue. The aircraft has a fixed size and obvious characteristics, so it is suitable for correlation filtering. Correlation filtering algorithms can extract features from input data to predict motion trajectories, and the calculation speed of correlation filterings is fast. Hence, such algorithms are advantageous for tracking targets in remote sensing images. In this article, an antidrift multifilter tracker based on a correlation filter and the Kalman filter is proposed for this purpose. This article proposes a temporal consistency-constrained background-aware correlation filter algorithm based on temporal regularization that resists the model drift caused by clouds by using motion information to correct it. Experimental results show that our proposed method shows improved antidrift performance compared with other advanced tracking methods in cases of cloud occlusion and stable performance in other complex conditions. We believe that our model will be helpful for researchers who are interested in object tracking in satellite video, especially for processing satellite video data with cloud occlusion.

To date, few-shot object detection methods have received extensive attention in the field of remote sensing, and no relevant research has been conducted using satellite videos. It is difficult to identify foreground objects in satellite videos duo to their small size and low contrast and the domain differences between base and novel classes under few-shot conditions. In this paper, we propose a few-shot aircraft detection method with a feature scale selection pyramid and proposal contrastive learning for satellite videos. Specifically, a feature scale selection pyramid network (FSSPN) is constructed to replace the traditional feature pyramid network (FPN), which alleviates the limitation of the inconsistencies in gradient computation between different layers for small-scale objects. In addition, we add proposal contrastive learning items to the loss function to achieve more robust representations of objects. Moreover, we expand the freezing parameters of the network in the fine-tuning stage to reduce the interference of visual differences between the base and novel classes. An evaluation of large-scale experimental data showed that the proposed method makes full use of the advantages of the two-stage fine-tuning strategy and the characteristics of satellite video to enhance the few-shot detection performance. [ABSTRACT FROM AUTHOR]

Video satellites can stare at target areas on the Earth's surface to obtain high-temporal-resolution remote sensing videos, which make it possible to track objects in satellite videos. However, it should be noted that the object size in satellite videos is usually small and has less textural property, and the moving objects in satellite videos are easily occluded, which puts forward higher requirements for the tracker. In order to solve the above problems, consider that the remote sensing image contains rich road information, which can be used to constrain the trajectory of the object in a satellite video, this paper proposes an improved Kernel Correlation Filter (KCF) assisted by road information to track small objects, especially when the object is occluded. Specifically, the contributions of this paper are as follows: First, the tracking confidence module is reconstructed, which integrates the peak response and the average peak correlation energy of the response map to more accurately judge whether the object is occluded. Then, an adaptive Kalman filter is designed to adaptively adjust the parameters of the Kalman filter according to the motion state of the object, which improves the robustness of tracking and reduces the tracking drift after the object is occluded. Last but not least, an object tracking strategy assisted by road information is recommended, which searches for objects with road information as constraints, to locate objects more accurately. After the above improvements, compared with the KCF tracker, our method improves the tracking precision by 35.9% and the tracking success rate by 18.1% with the tracking rate at a speed of 300 frames per second, which meets the real-time requirements. [ABSTRACT FROM AUTHOR]

Remote sensing object tracking (RSOT) is a novel and challenging problem due to the negative effects of weak features and background noise. In this article, from the perspective of attention-focus deep learning, we propose a joint Siamese attention-aware network (JSANet) for efficient remote sensing tracking which contains both the self-attention and cross-attention modules. First, the self-attention modules we propose emphasize the interdependent channel-wise coefficient via channel attention and conduct corresponding space transformation of spatial domain information with spatial attention. Second, the cross-attention is designed to aggregate rich contextual interdependencies between the Siamese branches via channel attention and excavate association produces reliable correspondence with spatial attention. In addition, a composite feature combine strategy is designed to fuse multiple attention features. Experimental results on the Jilin-1 satellite video datasets demonstrate that the proposed JSANet achieves state-of-the-art performance in terms of precision and success rate, demonstrating the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Satellite videos have recently served as a new data source for a wide range of applications in traffic management and military surveillance. Due to its wider coverage, satellite videos show more advantages in large-scale monitoring than ground surveillance videos. However, pseudomotion background and low-resolution targets pose new challenges to moving vehicle detection in satellite videos, resulting in poor performance of conventional target detection methods when applied to satellite videos. To overcome this difficulty, we propose a novel moving vehicle detection approach using adaptive motion separation and difference accumulated trajectory. Specifically, a new indicator is designed to assist adaptive separation of moving targets and background, considering the scale invariance of vehicles in satellite videos. Meanwhile, we offer a vehicle discrimination algorithm based on a differential accumulated trajectory to distinguish the moving vehicles from the pseudomotion background. Experimental results on two satellite video data sets demonstrate that the proposed approach achieves better detection performance over the state-of-the-art moving vehicle detection methods. [ABSTRACT FROM AUTHOR]

To date, few-shot object detection methods have received extensive attention in the field of remote sensing, and no relevant research has been conducted using satellite videos. It is difficult to identify foreground objects in satellite videos duo to their small size and low contrast and the domain differences between base and novel classes under few-shot conditions. In this paper, we propose a few-shot aircraft detection method with a feature scale selection pyramid and proposal contrastive learning for satellite videos. Specifically, a feature scale selection pyramid network (FSSPN) is constructed to replace the traditional feature pyramid network (FPN), which alleviates the limitation of the inconsistencies in gradient computation between different layers for small-scale objects. In addition, we add proposal contrastive learning items to the loss function to achieve more robust representations of objects. Moreover, we expand the freezing parameters of the network in the fine-tuning stage to reduce the interference of visual differences between the base and novel classes. An evaluation of large-scale experimental data showed that the proposed method makes full use of the advantages of the two-stage fine-tuning strategy and the characteristics of satellite video to enhance the few-shot detection performance.

Tracking moving objects from space-borne satellite videos is a new and challenging task. The main difficulty stems from the extremely small size of the target of interest. First, because the target usually occupies only a few pixels, it is hard to obtain discriminative appearance features. Second, the small object can easily suffer from occlusion and illumination variation, making the features of objects less distinguishable from features in surrounding regions. Current state-of-the-art tracking approaches mainly consider high-level deep features of a single frame with low spatial resolution, and hardly benefit from inter-frame motion information inherent in videos. Thus, they fail to accurately locate such small objects and handle challenging scenarios in satellite videos. In this article, we successfully design a lightweight parallel network with a high spatial resolution to locate the small objects in satellite videos. This architecture guarantees real-time and precise localization when applied to the Siamese Trackers. Moreover, a pixel-level refining model based on online moving object detection and adaptive fusion is proposed to enhance the tracking robustness in satellite videos. It models the video sequence in time to detect the moving targets in pixels and has ability to take full advantage of tracking and detecting. We conduct quantitative experiments on real satellite video datasets, and the results show the proposed HIGH-RESOLUTION SIAMESE NETWORK (HRSiam) achieves state-of-the-art tracking performance while running at over 30 FPS. [ABSTRACT FROM AUTHOR]

Deep learning methods have achieved the state-of-the-art performance of object detection and tracking in natural images, such as keypoint-based detectors and appearance/motion-based trackers. However, for small and blurry moving vehicles in satellite videos, keypoint-based detectors cause the missed detection of keypoints and incorrect keypoint matching. In terms of multi-object tracking, it is difficult to track the crowded similar vehicles stably only by using the appearance or motion information. To address these problems, a novel deep learning framework is proposed for moving vehicle detection and tracking in the satellite videos. It is comprised of the cross-frame keypoint-based detection network (CKDNet) and spatial motion information-guided tracking network (SMTNet). In CKDNet, a customized cross-frame module is designed to assist the detection of keypoints by exploiting complementary information between frames. Furthermore, CKDNet improves keypoint matching by incorporating size prediction around the keypoints and defining the soft mismatch suppression for out-of-size keypoint pairs. Based on high-quality detection, SMTNet can track the densely-packed vehicles effectively by constructing two-branch long short-term memories. It extracts not only spatial information of the same frame by considering the relative spatial relationship of neighboring vehicles, but also motion information among consecutive frames by calculating the movement velocity. Especially, it regresses virtual positions for missed or occluded vehicles and keeps on tracking these vehicles while they reappear. Experimental results on Jilin-1 and SkySat satellite videos demonstrate the effectiveness of the proposed detection and tracking methods. [ABSTRACT FROM AUTHOR]

Video satellites can stare at target areas on the Earth’s surface to obtain high-temporal-resolution remote sensing videos, which make it possible to track objects in satellite videos. However, it should be noted that the object size in satellite videos is usually small and has less textural property, and the moving objects in satellite videos are easily occluded, which puts forward higher requirements for the tracker. In order to solve the above problems, consider that the remote sensing image contains rich road information, which can be used to constrain the trajectory of the object in a satellite video, this paper proposes an improved Kernel Correlation Filter (KCF) assisted by road information to track small objects, especially when the object is occluded. Specifically, the contributions of this paper are as follows: First, the tracking confidence module is reconstructed, which integrates the peak response and the average peak correlation energy of the response map to more accurately judge whether the object is occluded. Then, an adaptive Kalman filter is designed to adaptively adjust the parameters of the Kalman filter according to the motion state of the object, which improves the robustness of tracking and reduces the tracking drift after the object is occluded. Last but not least, an object tracking strategy assisted by road information is recommended, which searches for objects with road information as constraints, to locate objects more accurately. After the above improvements, compared with the KCF tracker, our method improves the tracking precision by 35.9% and the tracking success rate by 18.1% with the tracking rate at a speed of 300 frames per second, which meets the real-time requirements.

As a new type of earth observation satellite approach, video satellites can continuously monitor an area of the Earth and acquire dynamic and abundant information by utilizing video imaging. Hence, video satellites can afford to track various objects of interest on the Earth's surface. Inspired by the capabilities of video satellites, this paper presents a novel method to track fast-moving objects in satellite videos based on the kernelized correlation filter (KCF) embedded with multi-feature fusion and motion trajectory compensation. The contributions of the suggested algorithm are multifold. First, a multi-feature fusion strategy is proposed to describe an object comprehensively, which is challenging for the single-feature approach. Second, a subpixel positioning method is developed to calculate the object’s position and overcome the poor tracking accuracy difficulties caused by inaccurate object localization. Third, introducing an adaptive Kalman filter (AKF) enables compensation and correction of the KCF tracker results and reduces the object’s bounding box drift, solving the moving object occlusion problem. Based on the correlation filtering tracking framework, combined with the above improvement strategies, our algorithm improves the tracking accuracy by at least 17% on average and the success rate by at least 18% on average compared to the KCF algorithm. Hence, our method effectively solves poor object tracking accuracy caused by complex backgrounds and object occlusion. The experimental results utilize satellite videos from the Jilin-1 satellite constellation and highlight the proposed algorithm's appealing tracking results against current state-of-the-art trackers regarding success rate, precision, and robustness metrics.

Satellite video scene classification (SVSC) is an advanced topic in the remote sensing field, which refers to determine the video scene categories from satellite videos. SVSC is an important and fundamental step for satellite video analysis and understanding, which provides priors for the presence of objects and dynamic events. In this paper, a two-stage framework is proposed to extract spatial features and motion features for SVSC. More specifically, the first stage is designed to extract spatial features for satellite videos. Representative frames are firstly selected based on the blur detection and spatial activity of satellite videos. Then the fine-tuned visual geometry group network (VGG-Net) is transferred to extract spatial features based on spatial content. The second stage is designed to build motion representation for satellite videos. The motion representation of moving targets in satellite videos is first built by the second temporal principal component of principal component analysis (PCA). Second, features from the first fully connected layer of VGG-Net are used as high-level spatial representation for moving targets. Third, a small network of long and short term memory (LSTM) is further designed for encoding temporal information. Two-stage features respectively characterize spatial and temporal patterns of satellite scenes, which are finally fused for SVSC. A satellite video dataset is built for video scene classification, including 7209 video segments and covering 8 scene categories. These satellite videos are from Jilin-1 satellites and Urthecast. The experimental results show the efficiency of our proposed framework for SVSC. [ABSTRACT FROM AUTHOR]

As a new method of Earth observation, video satellite is capable of monitoring specific events on the Earth’s surface continuously by providing high-temporal resolution remote sensing images. The video observations enable a variety of new satellite applications such as object tracking and road traffic monitoring. In this article, we address the problem of fast object tracking in satellite videos, by developing a novel tracking algorithm based on correlation filters embedded with motion estimations. Based on the kernelized correlation filter (KCF), the proposed algorithm provides the following improvements: 1) proposing a novel motion estimation (ME) algorithm by combining the Kalman filter and motion trajectory averaging and mitigating the boundary effects of KCF by using this ME algorithm and 2) solving the problem of tracking failure when a moving object is partially or completely occluded. The experimental results demonstrate that our algorithm can track the moving object in satellite videos with 95% accuracy. [ABSTRACT FROM AUTHOR]

The emerging satellite videos provide the opportunity to detect moving objects and track their trajectories, which were not possible for remotely sensed imagery with limited temporal resolution. So far, most studies using satellite video data have been concentrated on traffic monitoring through detecting and tracking moving cars, whereas the studies on other moving objects such as airplanes are limited. In this paper, an integrated method for monitoring moving airplanes from a satellite video is proposed. First, we design a normalized frame difference labeling (NFDL) algorithm to detect moving airplanes, which adopts a non-recursive strategy to deliver stable detection throughout the whole video. Second, the template matching (TM) technique is utilized for tracking the detected moving airplanes in the frame sequence by improved similarity measures (ISMs) with better rotation invariance and model drift suppression ability. Template matching with improved similarity measures (TM-ISMs) is further implemented to handle the leave-the-scene problem. The developed method is tested on a satellite video to detect and track eleven moving airplanes. Our NFDL algorithm successfully detects all the moving airplanes with the highest F1 score of 0.88 among existing algorithms. The performance of TM-ISMs is compared with both its traditional counterparts and other state-of-the-art tracking algorithms. The experimental results show that TM-ISMs can handle both rotation and leave-the-scene problems. Moreover, TM-ISMs achieve a very high tracking accuracy of 0.921 and the highest tracking speed of 470.62 frames per second.

In recent years, satellites capable of capturing videos have been developed and launched to provide high definition satellite videos that enable applications far beyond the capabilities of remotely sensed imagery. Moving object detection and moving object tracking are among the most essential and challenging tasks, but existing studies have mainly focused on vehicles. To accurately detect and then track more complex moving objects, specifically airplanes, we need to address the challenges posed by the new data. First, slow-moving airplanes may cause foreground aperture problem during detection. Second, various disturbances, especially parallax motion, may cause false detection. Third, airplanes may perform complex motions, which requires a rotation-invariant and scale-invariant tracking algorithm. To tackle these difficulties, we first develop an Improved Gaussian-based Background Subtractor (IPGBBS) algorithm for moving airplane detection. This algorithm adopts a novel strategy for background and foreground adaptation, which can effectively deal with the foreground aperture problem. Then, the detected moving airplanes are tracked by a Primary Scale Invariant Feature Transform (P-SIFT) keypoint matching algorithm. The P-SIFT keypoint of an airplane exhibits high distinctiveness and repeatability. More importantly, it provides a highly rotation-invariant and scale-invariant feature vector that can be used in the matching process to determine the new locations of the airplane in the frame sequence. The method was tested on a satellite video with eight moving airplanes. Compared with state-of-the-art algorithms, our IPGBBS algorithm achieved the best detection accuracy with the highest F1 score of 0.94 and also demonstrated its superiority on parallax motion suppression. The P-SIFT keypoint matching algorithm could successfully track seven out of the eight airplanes. Based on the tracking results, movement trajectories of the airplanes and their dynamic properties were also estimated.

Current new developments in remote sensing imagery enable satellites to capture videos from space. These satellite videos record the motion of vehicles over a vast territory, offering significant advantages in traffic monitoring systems over ground-based systems. However, detecting vehicles in satellite videos are challenged by the low spatial resolution and the low contrast in each video frame. The vehicles in these videos are small, and most of them are blurred into their background regions. While region proposals are often generated for efficient target detection, they have limited performance on satellite videos. To meet this challenge, we propose a Local Region Proposing approach (LRP) with three steps in this study. A video frame is segmented into semantic regions first and possible targets are then detected in these coarse scale regions. A discrete Histogram Mixture Model (HistMM) is proposed in the third step to narrow down the region proposals by quantifying their likelihoods towards the target category, where the training is conducted on positive samples only. Experiment results demonstrate that LRP generates region proposals with improved target recall rates. When a slim Fast-RCNN detector is applied, LRP achieves better detection performance over the state-of-the-art approaches tested.

As a new type of earth observation satellite approach, video satellites can continuously monitor an area of the Earth and acquire dynamic and abundant information by utilizing video imaging. Hence, video satellites can afford to track various objects of interest on the Earth's surface. Inspired by the capabilities of video satellites, this paper presents a novel method to track fast-moving objects in satellite videos based on the kernelized correlation filter (KCF) embedded with multi-feature fusion and motion trajectory compensation. The contributions of the suggested algorithm are multifold. First, a multi-feature fusion strategy is proposed to describe an object comprehensively, which is challenging for the single-feature approach. Second, a subpixel positioning method is developed to calculate the object's position and overcome the poor tracking accuracy difficulties caused by inaccurate object localization. Third, introducing an adaptive Kalman filter (AKF) enables compensation and correction of the KCF tracker results and reduces the object's bounding box drift, solving the moving object occlusion problem. Based on the correlation filtering tracking framework, combined with the above improvement strategies, our algorithm improves the tracking accuracy by at least 17% on average and the success rate by at least 18% on average compared to the KCF algorithm. Hence, our method effectively solves poor object tracking accuracy caused by complex backgrounds and object occlusion. The experimental results utilize satellite videos from the Jilin-1 satellite constellation and highlight the proposed algorithm's appealing tracking results against current state-of-the-art trackers regarding success rate, precision, and robustness metrics. [ABSTRACT FROM AUTHOR]

The emerging satellite videos provide the opportunity to detect moving objects and track their trajectories, which were not possible for remotely sensed imagery with limited temporal resolution. So far, most studies using satellite video data have been concentrated on traffic monitoring through detecting and tracking moving cars, whereas the studies on other moving objects such as airplanes are limited. In this paper, an integrated method for monitoring moving airplanes from a satellite video is proposed. First, we design a normalized frame difference labeling (NFDL) algorithm to detect moving airplanes, which adopts a non-recursive strategy to deliver stable detection throughout the whole video. Second, the template matching (TM) technique is utilized for tracking the detected moving airplanes in the frame sequence by improved similarity measures (ISMs) with better rotation invariance and model drift suppression ability. Template matching with improved similarity measures (TM-ISMs) is further implemented to handle the leave-the-scene problem. The developed method is tested on a satellite video to detect and track eleven moving airplanes. Our NFDL algorithm successfully detects all the moving airplanes with the highest F1 score of 0.88 among existing algorithms. The performance of TM-ISMs is compared with both its traditional counterparts and other state-of-the-art tracking algorithms. The experimental results show that TM-ISMs can handle both rotation and leave-the-scene problems. Moreover, TM-ISMs achieve a very high tracking accuracy of 0.921 and the highest tracking speed of 470.62 frames per second. [ABSTRACT FROM AUTHOR]

In recent years, satellites capable of capturing videos have been developed and launched to provide high definition satellite videos that enable applications far beyond the capabilities of remotely sensed imagery. Moving object detection and moving object tracking are among the most essential and challenging tasks, but existing studies have mainly focused on vehicles. To accurately detect and then track more complex moving objects, specifically airplanes, we need to address the challenges posed by the new data. First, slow-moving airplanes may cause foreground aperture problem during detection. Second, various disturbances, especially parallax motion, may cause false detection. Third, airplanes may perform complex motions, which requires a rotation-invariant and scale-invariant tracking algorithm. To tackle these difficulties, we first develop an Improved Gaussian-based Background Subtractor (IPGBBS) algorithm for moving airplane detection. This algorithm adopts a novel strategy for background and foreground adaptation, which can effectively deal with the foreground aperture problem. Then, the detected moving airplanes are tracked by a Primary Scale Invariant Feature Transform (P-SIFT) keypoint matching algorithm. The P-SIFT keypoint of an airplane exhibits high distinctiveness and repeatability. More importantly, it provides a highly rotation-invariant and scale-invariant feature vector that can be used in the matching process to determine the new locations of the airplane in the frame sequence. The method was tested on a satellite video with eight moving airplanes. Compared with state-of-the-art algorithms, our IPGBBS algorithm achieved the best detection accuracy with the highest F1 score of 0.94 and also demonstrated its superiority on parallax motion suppression. The P-SIFT keypoint matching algorithm could successfully track seven out of the eight airplanes. Based on the tracking results, movement trajectories of the airplanes and their dynamic properties were also estimated. [ABSTRACT FROM AUTHOR]

Current new developments in remote sensing imagery enable satellites to capture videos from space. These satellite videos record the motion of vehicles over a vast territory, offering significant advantages in traffic monitoring systems over ground-based systems. However, detecting vehicles in satellite videos are challenged by the low spatial resolution and the low contrast in each video frame. The vehicles in these videos are small, and most of them are blurred into their background regions. While region proposals are often generated for efficient target detection, they have limited performance on satellite videos. To meet this challenge, we propose a Local Region Proposing approach (LRP) with three steps in this study. A video frame is segmented into semantic regions first and possible targets are then detected in these coarse scale regions. A discrete Histogram Mixture Model (HistMM) is proposed in the third step to narrow down the region proposals by quantifying their likelihoods towards the target category, where the training is conducted on positive samples only. Experiment results demonstrate that LRP generates region proposals with improved target recall rates. When a slim Fast-RCNN detector is applied, LRP achieves better detection performance over the state-of-the-art approaches tested. [ABSTRACT FROM AUTHOR]