Moving Vehicle Information Extraction from Single-Pass WorldView-2 Imagery Based on ERGAS-SNS Analysis

Gao, Feng; Li, Bo; Xu, Qizhi; Zhong, Chen

doi:10.3390/rs6076500

Cited by 21 publications

(13 citation statements)

References 19 publications

(31 reference statements)

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“…Techniques to track moving objects (such as vehicles) in WorldView-2 images are illustrated in [79] and [80]. The main idea is based on the time gap between different banks of filters.…”

Section: Miscellaneous Applicationsmentioning

confidence: 99%

Challenges and Opportunities of Multimodality and Data Fusion in Remote Sensing

Mura

Prasad

Pacifici³

et al. 2015

Proc. IEEE

200

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International audience—Remote sensing is one of the most common ways to extract relevant information about the Earth and our environment. Remote sensing acquisitions can be done by both active (synthetic aperture radar, LiDAR) and passive (optical and thermal range, multispectral and hyperspectral) devices. According to the sensor, a variety of information about the Earth's surface can be obtained. The data acquired by these sensors can provide information about the structure (optical, synthetic aperture radar), elevation (LiDAR) and material content (multi and hyperspectral) of the objects in the image. Once considered together their comple-mentarity can be helpful for characterizing land use (urban analysis, precision agriculture), damage detection (e.g., in natural disasters such as floods, hurricanes, earthquakes, oil-spills in seas), and give insights to potential exploitation of resources (oil fields, minerals). In addition, repeated acquisitions of a scene at different times allows one to monitor natural resources and environmental variables (vegetation phenology, snow cover), anthropological effects (urban sprawl, deforestation), climate changes (desertification, coastal erosion) among others. In this paper, we sketch the current opportunities and challenges related to the exploitation of multimodal data for Earth observation. This is done by leveraging the outcomes of the Data Fusion contests, organized by the IEEE Geoscience and Remote Sensing Society since 2006. We will report on the outcomes of these contests, presenting the multimodal sets of data made available to the community each year, the targeted applications and an analysis of the submitted methods and results: How was multimodality considered and integrated in the processing chain? What were the improvements/new opportunities offered by the fusion? What were the objectives to be addressed and the reported solutions? And from this, what will be the next challenges

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“…Techniques to track moving objects (such as vehicles) in WorldView-2 images are illustrated in [79] and [80]. The main idea is based on the time gap between different banks of filters.…”

Section: Miscellaneous Applicationsmentioning

confidence: 99%

Challenges and Opportunities of Multimodality and Data Fusion in Remote Sensing

Mura

Prasad

Pacifici³

et al. 2015

Proc. IEEE

200

View full text Add to dashboard Cite

show abstract

“…Then there is a slight time lag between the acquisitions of the PAN and MS images, which can be used to extract the moving information of targets. The single-pass imagery has been successfully used to extract motion information of vehicles and planes [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 ], while the time lag is too small to detect low speed moving vessels. Motion parameters can also be estimated using along-track sequential or stereo satellite images, which are taken by agile remote sensing satellites such as WorldView-2 with multi-angular look capability [ 12 , 13 , 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Ship-Tracking Method for GF-4 Satellite Sequential Images

Yao

Liu

2018

Sensors

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The geostationary remote sensing satellite has the capability of wide scanning, persistent observation and operational response, and has tremendous potential for maritime target surveillance. The GF-4 satellite is the first geostationary orbit (GEO) optical remote sensing satellite with medium resolution in China. In this paper, a novel ship-tracking method in GF-4 satellite sequential imagery is proposed. The algorithm has three stages. First, a local visual saliency map based on local peak signal-to-noise ratio (PSNR) is used to detect ships in a single frame of GF-4 satellite sequential images. Second, the accuracy positioning of each potential target is realized by a dynamic correction using the rational polynomial coefficients (RPCs) and automatic identification system (AIS) data of ships. Finally, an improved multiple hypotheses tracking (MHT) algorithm with amplitude information is used to track ships by further removing the false targets, and to estimate ships’ motion parameters. The algorithm has been tested using GF-4 sequential images and AIS data. The results of the experiment demonstrate that the algorithm achieves good tracking performance in GF-4 satellite sequential images and estimates the motion information of ships accurately.

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“…Determining changed areas in images of the same scene taken at different points in time is of considerable interest given that it offers a large number of applications in various disciplines [2], including: video-surveillance [3], medical diagnoses and treatment [4], vehicle driving support [5] and remote detection.…”

Section: Introductionmentioning

confidence: 99%

Assessment of Data Fusion Algorithms for Earth Observation Change Detection Processes

Molina

Martínez

Morillo

et al. 2016

Sensors

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In this work a parametric multi-sensor Bayesian data fusion approach and a Support Vector Machine (SVM) are used for a Change Detection problem. For this purpose two sets of SPOT5-PAN images have been used, which are in turn used for Change Detection Indices (CDIs) calculation. For minimizing radiometric differences, a methodology based on zonal “invariant features” is suggested. The choice of one or the other CDI for a change detection process is a subjective task as each CDI is probably more or less sensitive to certain types of changes. Likewise, this idea might be employed to create and improve a “change map”, which can be accomplished by means of the CDI’s informational content. For this purpose, information metrics such as the Shannon Entropy and “Specific Information” have been used to weight the changes and no-changes categories contained in a certain CDI and thus introduced in the Bayesian information fusion algorithm. Furthermore, the parameters of the probability density functions (pdf’s) that best fit the involved categories have also been estimated. Conversely, these considerations are not necessary for mapping procedures based on the discriminant functions of a SVM. This work has confirmed the capabilities of probabilistic information fusion procedure under these circumstances.

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Moving Vehicle Information Extraction from Single-Pass WorldView-2 Imagery Based on ERGAS-SNS Analysis

Cited by 21 publications

References 19 publications

Challenges and Opportunities of Multimodality and Data Fusion in Remote Sensing

Challenges and Opportunities of Multimodality and Data Fusion in Remote Sensing

A Novel Ship-Tracking Method for GF-4 Satellite Sequential Images

Assessment of Data Fusion Algorithms for Earth Observation Change Detection Processes

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