XXII ISPRS Congress 2012: Technical Commission VII

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Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Document type:: Multivolume work

Volume

Persistent identifier:: 1663821976

Title:: Technical Commission VII

Scope:: 546 Seiten

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663821976

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(39,B7)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Corporations:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Adapter:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Founder of work:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Other corporate:: International Society for Photogrammetry and Remote Sensing, Congress, 22., 2012, Melbourne; International Society for Photogrammetry and Remote Sensing

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VII/5: METHODS FOR CHANGE DETECTION AND PROCESS MODELLING]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: AUTOMATIC MOVING VEHICLE'S INFORMATION EXTRACTION FROM ONE-PASS WORLDVIEW-2 SATELLITE IMAGERY Rakesh Kumar Mishra

Document type:: Multivolume work

Structure type:: Chapter

vel ind nge iria pp. ing ng, rtz, /Sis yk [00 ol. ect ster ote International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia AUTOMATIC MOVING VEHICLE'S INFORMATION EXTRACTION FROM ONE-PASS WORLDVIEW-2 SATELLITE IMAGERY Rakesh Kumar Mishra Department of Geodesy and Geomatics Engineering, University of New Brunswick, NB, CANADA rakesh.mishra@unb.ca Commission VII, WG VII/5 KEY WORDS: Satellite images, WorldView-2, vehicles detection, vehicle information, traffic, AdaBoost. ABSTRACT: There are several applications of vehicle information (position, speed, and direction). WorldView-2 satellite has three sensors: one Pan and two MS (MS-1: BGRNI, Pan, and MS-2:CYREN2). Because of a slight time gap in acquiring images from these sensors, the WorldView-2 images capture three different positions of the moving vehicles. This paper proposes a new technique to extract the vehicle information automatically by utilizing the small time gap in WorldView-2 sensors. A PCA-based technique has been developed to automatically detect moving vehicles from MS-1 and MS-2 images. The detected vehicles are used to limit the search space of the adaptive boosting (AdaBoost) algorithm in accurately determining the positions of vehicles in the images. Then, RPC sensor model of WorldView-2 has been used to determine vehicles’ ground positions from their image positions to calculate speed and direction. The technique has been tested on a Worldview-2 image. A vehicle detection rate of over 95% has been achieved. The results of vehicles’ speed calculations are reliable. This technique makes it feasible to use satellite images for traffic applications on an operational basis. 1. INTRODUCTION The increasing volume of already-high traffic loads creates new challenges for traffic management and planning. Moving vehicle information (position, speed, and direction) is crucial for traffic planning, security surveillance, and military applications. Today’s road systems are equipped with a suite of sensors for monitoring traffic status, such as induction loops, overhead radar sensors and video sensors. While they all deliver reliable measurements, the results are merely point-based in nature. On the other hand, information provided by remote sensing techniques covers a larger area and thus could often be useful for better understanding the dynamics of the traffic. The launch of high resolution satellites such as QuickBird and WorldView- 2 has made it feasible to use satellite images for traffic applications. These satellites capture images with a spatial resolution better than 1-m and hence can be used to extract road traffic information. Furthermore, the high resolution satellite images give a synoptic view of complex traffic situations and the associated context. In the past, several efforts (Gerhardinger et al., 2005; Sharma et al., 2006; Jin and Davis, 2007; Zheng et al., 2006; Zheng and Li, 2007) have been made to detect vehicles from HR satellite imagery. A few attempts (Xiong and Zhang, 2008; Leitloff and Hinz, 2010; Liu et al, 2010) have been made to determine vehicle speeds using QuickBird imagery. These methods utilize the small time interval between the acquisition of Pan and MS images by QuickBird sensors. Xiong and Zhang (2008) developed a methodology to determine vehicle's ground position, speed and direction using QuickBird Pan and MS images. However, the major limitation of the Xiong and Zhang (2008) approach is that in this method there is a need to select vehicles’ central positions manually from Pan and MS images. Leitloff and Hinz (2010) have used adaptive boosting (AdaBoost) classification technique to detect single vehicles from Pan images and then the corresponding vehicles from MS images have been detected using the similarity matching approach. Whereas, Liu et al. (2010) have used an object-based method to detect single vehicles from Pan images and then the corresponding vehicles from MS images have been detected using the area correlation method. Both aforementioned approaches have achieved a fair level of accuracy in vehicle detection from Pan images. However, accuracy of vehicle detection from MS images is quite low which leads to high error in determining vehicles” position is MS images. As the time interval between the acquisition of Pan and MS images is very short, a very small error in vehicles’ position determination will lead to a very high error in vehicles” speed computation. The recently-launched high resolution satellite, World View-2, has three sensors: one Pan and two MS (MS-1: BGRNI, Pan, and MS-2:CYREN2). Because of a slight time gap in acquiring images from these sensors, the WorldView-2 images capture three different positions of the moving objects (vehicles) and static objects remain at the same position. Therefore, theoretically it is possible to detect moving vehicles from the WorldView-2 imagery. Practically, these calculations bring many challenges in the image processing domain. The spatial resolution of the MS image is low (2m) which makes vehicle extraction a difficult task. Furthermore, MS-1 and MS-2 images constitute different spectral wavelengths; therefore the existing change detection methods are incapable of detecting moving vehicles from the images. In addition, the accurate determination of ground positions of a moving vehicle available in each image is important for accurate speed computation. This paper proposes a completely different and new methodology to automatically and accurately extract moving vehicle's information (position, speed and direction) from MS-1 and MS-2 images captured by the WorldView-2 satellite in one pass. A motion detection algorithm has been developed which looks into MS-1 and MS-2 images and detects the objects which are in motion. The novelty of this algorithm is that it is completely automatic and there is no need for road extraction prior to vehicle detection. In earlier vehicle detection methods, prior to the vehicle detection, there is a need to extract roads either manually or from GIS data. A vehicle detection rate of

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