Vehicle Detection and Tracking in Remote Sensing Satellite Vidio based on Dynamic Association

Zhang, Jinyue; Zhang, Xiangrong; Tang, Xu; Huang, Zhongjian; Jiao, Licheng

doi:10.1109/multi-temp.2019.8866890

Cited by 5 publications

(5 citation statements)

References 15 publications

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“…Seyed Ali et al [232] applied background subtraction to detect moving vehicles and estimate the trajectory, speed, and other information of the vehicle.. Zhang et al [233] also used background subtraction to detect moving vehicles and apply dynamic association methods to match the objects. Ao et al [234] established a local noise model to distinguish vehicle objects through an exponential probability distribution.…”

Section: Multi-oriented Object Representationmentioning

confidence: 99%

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Jiao

Huang

Liu

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Brain-inspired algorithms have become a new trend in next-generation artificial intelligence. Through research on brain science, the intelligence of remote sensing algorithms can be effectively improved. This paper summarizes and analyzes the essential properties of brain cognise learning and the recent advance of remote sensing interpretation. Firstly, this paper introduces the structural composition and the properties of the brain. Then, five represent brain-inspired algorithms are studied, including multiscale geometry analysis, compressed sensing, attention mechanism, reinforcement learning, and transfer learning. Next, this paper summarizes the data types of remote sensing, the development of typical applications of remote sensing interpretation and the implementations of remote sensing, including datasets, software, and hardware. Finally, the top ten open problems and the future direction of brain-inspired remote sensing interpretation are discussed. This work aims to comprehensively review the brain mechanisms and the development of remote sensing and to motivate future research on brain-inspired remote sensing interpretation.

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Section: Multi-oriented Object Representationmentioning

confidence: 99%

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Jiao

Huang

Liu

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…One step-based [92] 2014 Two paralleled trackers for initialization and tracking [93] 2021 3D variation regularization + PCA Two step-based [94] 2018 Global data association approach [95] 2019 DTS for traffic parameters estimating [96] 2019 DTS for vehicle tracking…”

Section: Ref Year Descriptionmentioning

confidence: 99%

“…Meanwhile, Ref. [96] offered an efficient DTS to track vehicles in multi-temporal remote sensing images. In the detection phase, the authors applied background subtraction, reduced searching space, and combined road prior information to improve detection accuracy.…”

Section: Ref Year Descriptionmentioning

confidence: 99%

Object Tracking Based on Satellite Videos: A Literature Review

et al. 2022

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Video satellites have recently become an attractive method of Earth observation, providing consecutive images of the Earth’s surface for continuous monitoring of specific events. The development of on-board optical and communication systems has enabled the various applications of satellite image sequences. However, satellite video-based target tracking is a challenging research topic in remote sensing due to its relatively low spatial and temporal resolution. Thus, this survey systematically investigates current satellite video-based tracking approaches and benchmark datasets, focusing on five typical tracking applications: traffic target tracking, ship tracking, typhoon tracking, fire tracking, and ice motion tracking. The essential aspects of each tracking target are summarized, such as the tracking architecture, the fundamental characteristics, primary motivations, and contributions. Furthermore, popular visual tracking benchmarks and their respective properties are discussed. Finally, a revised multi-level dataset based on wpafb videos is generated and quantitatively evaluated for future development in the satellite video-based tracking area. In addition, 54.3% of the tracklets with lower ds are selected and renamed as the Easy group, while 27.2% and 18.5% of the tracklets are grouped into the Medium-ds group and the Hard-ds group, respectively.

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“…Remote sensing is a technology that collects information about the Earth in a noncontact way [1]. Optical remote sensing images have a wide range of applications in environmental monitoring [2], military target recognition [3], moving target tracking [4], and resource exploration [5]. However, due to the inherent properties of remote sensing imaging equipment and the processes of storage, compression, and transmission, remote sensing images will be damaged by random signals, resulting in image degradation.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Denoising Based on Deep and Shallow Feature Fusion and Attention Mechanism

Han

Zhao

et al. 2022

Remote Sensing

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Optical remote sensing images are widely used in the fields of feature recognition, scene semantic segmentation, and others. However, the quality of remote sensing images is degraded due to the influence of various noises, which seriously affects the practical use of remote sensing images. As remote sensing images have more complex texture features than ordinary images, this will lead to the previous denoising algorithm failing to achieve the desired result. Therefore, we propose a novel remote sensing image denoising network (RSIDNet) based on a deep learning approach, which mainly consists of a multi-scale feature extraction module (MFE), multiple local skip-connected enhanced attention blocks (ECA), a global feature fusion block (GFF), and a noisy image reconstruction block (NR). The combination of these modules greatly improves the model’s use of the extracted features and increases the model’s denoising capability. Extensive experiments on synthetic Gaussian noise datasets and real noise datasets have shown that RSIDNet achieves satisfactory results. RSIDNet can improve the loss of detail information in denoised images in traditional denoising methods, retaining more of the higher-frequency components, which can have performance improvements for subsequent image processing.

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Vehicle Detection and Tracking in Remote Sensing Satellite Vidio based on Dynamic Association

Cited by 5 publications

References 15 publications

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Object Tracking Based on Satellite Videos: A Literature Review

Remote Sensing Image Denoising Based on Deep and Shallow Feature Fusion and Attention Mechanism

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