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

over 95% has been achieved with a high reliability. The positions of detected moving vehicles have been inputted to the AdaBoost machine learning algorithm to further improve the accuracy of vehicles’ image positions. This is because the vehicles’ speed calculation is highly dependent on the accuracy of vehicles’ image position. Then, ground positions of each detected vehicle from MS-1 and MS-2 images have been computed using sensor model (RPC) provided by WorldView-2 satellite. This paper begins by discussing the methodology developed to detect moving vehicles. Then, methodology to compute vehicles’ information is discussed. Finally, results and conclusions are presented. 2. METHODOLOGY In this paper, a Principal Component Analysis (PCA) based method has been developed to detect moving vehicles from Worldview-2 MS-1 and MS-2 images. The workflow of the methodology developed is shown in Figure 1. Then, AdaBoost learning algorithm based method has been developed to compute vehicles’ information. The work flow of vehicles’ information computation is shown in Figure 5. 2.1 Study Area and Data used WorldView-2 imagery of a part of Moncton, a city in New Brunswick, Canada, has been used for this study. This WorldView-2 image was provided by DigitalGlobe® Inc. to Bahram Salehi (University of New Brunswick) through “The DigitalGlobe 8-Band Research Challenge" contest. The image was taken on October 5, 2010. The WorldView-2 imagery includes Pan image MS-1 (BGRNI) image, and MS-2 (CYREN2) image. The MS-1 and MS-2 bands are stacked together as one MS image with 8-bands. 2.2 Image Resampling WorldView-2 MS images have spatial resolution of 2m; therefore, small objects like vehicles are not clearly identifiable. To make vehicles more identifiable, the MS image has been re- sampled to 0.5m using cubic convolution resampling method. 2.3 PCA Computation MS-1 and MS-2 images constitute different spectral wavelengths; therefore change detection methods are incapable of detecting moving vehicles. In this paper, principal components of MS-1 and MS-2 images have been computed. As shown in Figure-2 and Figure-3, vehicles are more distinguishable in the second principal component. Therefore, second principal components of MS-1 image (MS-1: PCA2) and MS-2 images (MS-2: PCA2) have been selected for further processing. Also, the principal component of MS image, which has 8 bands stacked together, has been computed. As shown in Figure-4, the vehicles are again more distinguishable in the second principal component (MS: PCA2). Furthermore, as shown in Figure-4, the second principal component has two positions of one moving vehicle. This result is very useful for detecting moving vehicles from the MS-1 and MS-2 images. Finally, after PCA computations three images, MS-1: PCA2, MS-2: PCA2, MS: PCA2, have been selected for further processing. 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 | WorldView-2: MS image | image Resampling | | I | MS-1:BGRN-1 | |MS-2:CYREN-2| | Ms |] | = | Y | PCA Computation | l Y Y | MS-i:PCA-2 | | MS:PCA-2 | | MS-2:PCA-2 | | | Y Threshold | Band Adjustment | | i J | (MS: PCA-2) - (MS2:PCA-2) | | (MS: PCA-2) - (MSI: PCA-2) | | i Y | image Filtering | emt Otsu Threshold de Yl | MS-1: Moving Vehides | | | MS-2: Moving Vehicles | Figure 1: Workflow for vehicle detection Threshold 2.4 Band Adjustment The histograms of MS-1:PCA2, MS-2:PCA2 and MS: PCA2 images have been adjusted to a common mean and standard deviation. This process has improved the accuracy of moving vehicle detection. 2.5 Moving Vehicle Detection After the Band Adjustment, a change detection process has been applied to the images to detect moving vehicles. Change detection is an important process in remote sensing applications (Copping et al., 2004; Tronin, 2006). In the change detection process, two images of the same scene captured at different time instances are used to detect changes. Therefore, change detection can be expressed as: Lou at b (1) Where 7,; and 7,; are the images captured at time t, and t; and a and b are the constant scalar values. The aim of change detection process is to model the constants a and b. A variety of change detection algorithms are available; however, in this study, it has been found that the differencing method for change detection is efficient and best suited for detecting vehicles from WorldView-2 MS imagery. As shown in Figure-2 and Figure-3, the MS-1:PCA2 and MS-2:PCA2 images have different positions of a moving vehicle whereas MS: PCA2 image (Figure-4) has two different positions of the same moving vehicle. Thus, moving vehicles from the MS-1 image have been detected using equation (2) and moving vehicles from the MS-2 image have been detected using equation (3). (MS-1 Image)Moving vehicles = (MS: PCA2) — (MS2: PCA2)-T, @) (MS-2 Image)Moving vehicles = (MS: PCA2) — (MSI: PCA2)-T; (3) Where parameters T, and T» are the thresholds which are used to eliminate outliers appears after differencing process.

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