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/4: METHODS FOR LAND COVER CLASSIFICATION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: LAND COVER INFORMATION EXTRACTION USING LIDAR DATA Ahmed Shaker, Nagwa El-Ashmawy

Document type:: Multivolume work

Structure type:: Chapter

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 LAND COVER INFORMATION EXTRACTION USING LIDAR DATA Ahmed Shaker ^ *, Nagwa El-Ashmawy ^^ ? Ryerson University, Civil Engineering Department, Toronto, Canada - (ahmed.shaker, nagwa.elashmawy)@ryerson.ca ? Survey Research Institute, National Water Research Center, Cairo, Egypt Commission VII, WG VII/4 KEY WORDS: LiDAR, intensity data, Land Cover classification, PCA. Light Detection and Ranging (LiDAR) systems are used intensively in terrain surface modelling based on the range data determined by the LiDAR sensors. LIDAR sensors record the distance between the sensor and the targets (range data) with a capability to record the strength of the backscatter energy reflected from the targets (intensity data). The LiDAR sensors use the near-infrared spectrum range which has high separability in the reflected energy from different targets. This characteristic is investigated to implement the LiDAR intensity data in land-cover classification. The goal of this paper is to investigate and evaluates the use of LIDAR data only (range and intensity data) to extract land cover information. Different bands generated from the LiDAR data (Normal Heights, Intensity Texture, Surfaces Slopes, and PCA) are combined with the original data to study the influence of including these layers on the classification accuracy. The Maximum likelihood classifier is used to conduct the classification process for the LIDAR Data as one of the best classification techniques from literature. A study area covering an urban district in Burnaby, British Colombia, Canada, is selected to test the different band combinations to extract four information classes: buildings, roads and parking areas, trees, and low vegetation (grass) areas. The results show that an overall accuracy of more than 70% can be achieved using the intensity data, and other auxiliary data generated from the range and intensity data. Bands of the Principle Component Analysis (PCA) are also created from the LiDAR original and auxiliary data. Similar overall accuracy of the results can be achieved using the four bands extracted from the Principal Component Analysis (PCA). 1. INTRODUCTION Light Detection and Ranging (LiDAR) is a remote sensing technique used mainly for 3D data acquisition of the Earth surface and its applications in the 3D City modelling and building extraction and recognition, (Haala & Brenner, 1999, Song et al, 2002, Brennan and Webster, 2006, Hui et al., 2008, and Yan & Shaker, 2010). LiDAR sensors transmit laser pulses in near infrared (NIR) spectrum range toward objects and record the reflected energy. The distances between the LiDAR sensor and the targets (range data) are calculated. The 3D coordinates of the collected points are calculated from the range data with the aid of other sensors (GPS, and IMU), (Ackerman, 1999). LiDAR is considered as highly precise and accurate vertical and horizontal data acquisition system (Brennan and Webster, 2006). The high accurate data are used for generating digital elevation and/or surface models (DTM/DSM), Kraus & Pfeifer, (1998) used LiDAR data to create DTM in wooded areas. The accuracy of the DTM extracted was 25 cm for flat areas, which is improved to 10 cm by refining the data processing method. In the last decade, substantial work is done to combine the LiDAR data with other external data such as aerial photos and satellite images for information extraction. Haala & Brenner (1999) combined LiDAR elevation data and a multi-spectral aerial photo (Green, Red and NIR bands) for building extraction using unsupervised classification technique. It was found that * Corresponding author: combining the multi-spectral aerial photo with the LiDAR elevation data improved the classification results significantly. LiDAR sensors not only record the time difference between sending and receiving signals; but they also record the backscattered energy from the targets (intensity data) in NIR spectrum range. A NIR image can be generated by interpolating the intensity data collected by the LiDAR sensors. With the capability to record the intensity of the reflected energy, definition of the classification of LiDAR data is not only referring to the separation of terrain and non-terrain features, but it includes the use of the intensity data for the classification of land covers as well. Hence, intensity data is investigated to be used to distinguish different target materials using various image classification techniques. Recently, the use of the LiDAR intensity and range data has been studied for data classification and feature extraction. The intensity data were used primarily as a complementary data for data visualization and interpretation. LIDAR intensity data are advantageous over the multi-spectral remote sensing data in avoiding the shadows appear in the multi-spectral data. This is because LiDAR sensor is an active sensor. Hui et al., 2008, used the intensity and height LiDAR data for land-cover classification. Supervised classification technique was used to differentiate four classes: Tree, Building, Bare Earth and Low Vegetation. It was observed that combining the intensity data with the height data is an effective method for LiDAR data classification. However, quantitative accuracy assessment was not included in that research work. Ahmed Shaker, Ryerson University, Department of Civil Engineering, 350 Victoria St., Toronto, Canada, M5B 2K3 E-mail: ahmed shaker@ryerson.ca Tel: +1 416 979 5000 (ext. 4658)

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