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/6: REMOTE SENSING DATA FUSION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: FAST OCCLUSION AND SHADOW DETECTION FOR HIGH RES OLUTION REMOTE SENSING IMAGE COMBINED WITH LIDAR POINT CLOUD Xiangyun Hu, Xiaokai Li

Document type:: Multivolume work

Structure type:: Chapter

sensing e and vember 1 based Acta model ed and 1 based sity of signal actions 7), pp. nalysis "nce of scenes. lachine aussian image (4), pp. sea ice IEEE 2), PP. is and rsity of nalysis sensed eatures mputer ndbook entific, design Sinica, arative IEEE p. 269- 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 FAST OCCLUSION AND SHADOW DETECTION FOR HIGH RES OLUTION REMOTE SENSING IMAGE COMBINED WITH LIDAR POINT CLOUD Xiangyun Hu * *, Xiaokai Li* ? School of Remote Sensing and Information Engineering, Wuhan University, 129 Luoyu Road, Wuhan, CHINA, 430079 Commission VIL, WG VII/6 KEY WORDS : Fast, Occlusion, Shadow, High Resolution, Remote Sensing, LiDAR ABSTRACT: The orthophoto is an important component of GIS database and has been applied in many fields. But occlusion and shadow causes the loss of feature information which has a great effect on the quality of images. One of the critical steps in true orthophoto generation is the detection of occlusion and shadow. Nowadays LiDAR can obtain the digital surface model (DSM) directly. Combined with this technology, image occlusion and shadow can be detected automatically. In this paper, the Z-Buffer is applied for occlusion detection. The shadow detection can be regarded as a same problem with occlusion detection cons idering the angle between the sun and the camera. However, the Z-Buffer algorithm is computationally expensive. And the volume of scanned data and remote sensing images is very large. Efficient algorithm is another challenge. M odern graphics processing unit (GPU) is much more powerful than central processing unit (CPU). We introduce this technology to sp eed up the Z-Buffer algorithm and get 7 times increase in speed compared with CPU. The results of experiments demonstrate that Z-Buffer algorithm plays well in occlusion and shadow detection combined with high density of point cloud and GPU can speed up the computation significantly. 1. INTRODUCTION The orthophoto is an important component of geographic information system (GIS) database and has been applied in many fields. That it has uniform scale and no relief displacement enables the users to measure distances and areas directly. However, the traditional generation of orthophotos is based on digital elevation models (DEM) which doesn't take the buildings and any other objects above the terrain into account. Occlusion and shadow effects caused by the abrupt change of surface height are major aspects of information degeneration in orthophotos (Rau et al, 2002). In true orthophotos, the occlusion and shadow should be detected and compensated. Light Detection and Ranging (LiDAR) integrates the Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS) with laser scanning and ranging technologies. It offers a directly method to measure the three-dimensional coordinates of points on ground objects and makes the creation of digital surface models (DSM) very efficient (Disa ef al., 201 1). Combined with this technology, image occlusion and shadow can be detected automatically. The Z-Buffer algorithm is one of the most popular methods of occlusion detection (Liang-Chen ef al., 2007, Bang ef al, 2007). It calculates the distances between the projection centre and object points. The closest object point is visible while others are occluded in one line of sight (Ambar ef al., 1998). The shadow detection can be regarded as a same problem with occlusion detection considering the angle between the sun and the camera (Rau ef al, 2002). However, this method is computationally intensive (Kato et al., 2010). On the other hand, *huxy @whu.edu.cn; phone 86 27 687-78010; fax 86 27 687-78086 399 the resolution of the remote sensing images and LiDAR point cloud is becoming higher and higher, making the large volume of data in remote sensing greater. It has been one of the main challenges in data processing. With the rapid development of computer hardware, the processing power is growing. Central Processing Unit (CPU) has entered the era of multi-core and it shows strong parallel computing power. And Graphic Processing Unit (GPU) has evolved into highly parallel, multi-threaded, many-core processors with tremendous computational horsepower and a very high memory bandwidth (NVIDIA, 2011). It is much more powerful than CPU in parallel computing capability. Since the release of Compute Unified Device Architecture (CUDA), it has become increasingly convenient and efficient to use GPUs to speed up applications. In this paper, the method of occlusion and shadow detection for high resolution remote sensing image combined with LIDAR point cloud is discussed and the GPU is introduced to accelerate the algorithm. 2. OCCLUSION AND SHADOWN DETECTION Occlusion detection is a problem of visibility analysis (Hablb et al., 2007). And the Z-Buffer algorithm is the most commonly used method. In the algorithm, an image matrix called z-buffer is used to store the distance (Z) between the projection centre and points in the object surface which correspond to the pixels in the image. An index matrix is also needed to denote the visibility of every point. Some points may be projected onto the same pixel. Calculate the distance and compare it with the existing

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