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

Recent researchers combine the laser data with other auxiliary data such as multispectral aerial photos or satellite images, USGS DEM, texture data, normalized height, and multiple- returns data. Charaniya et al, (2004) used LiDAR height and intensity data, height variation data, multiple-return data, USGS DEM, and luminance data of a panchromatic aerial imagery for land-cover classification. A supervised classifier was used to distinguish four classes: trees, grass, roads, and roofs. The effect of band combinations on the classification results was studied. It was observed that height variation affected positively the classification results of the high vegetation areas, Luminance and intensity data was useful for distinguishing the roads from the low vegetation areas, and the multiple-return differences slightly improved the classification of roads and buildings but reduced the accuracy of the other classes. Subsequently, researchers gave more attention to the intensity data and started to analyse the data and study different enhancing methods to remove the noise and improve the data interpretation. Song et al, (2002) examined different resampling techniques to convert LiDAR point data to grid image data which is filtered to remove the noise with minimum influence on the original data. The resampled grid is used to investigate the applicability of using the LiDAR intensity data for land- cover classification. It is concluded that the LiDAR intensity data contain noise that is needed to be removed. Radiometric correction of the intensity data was suggested in some of the recent literatures (Coren and Sterzia, 2006; Hófle and Pfeifer, 2007). The process mainly relies on the use of the laser range equation to convert the intensity data into the spectral reflectance with consideration of the scanning geometry, the atmospheric attenuation, and the background backscattering effects. After the radiometric correction, the homogeneity of the land cover is improved and thus enhances the performance of feature extraction and surface classification. Yan et al. (2012) evaluated the accuracy of different land cover classification scenarios by using the airborne LiDAR intensity data before and after radiometric correction. An accuracy improvement of 8% to 12% was found after applying the radiometric correction. This research investigates the use of the intensity data for land- cover information extraction. The Maximum Likelihood supervised classification technique is proposed and applied on two different study areas, and classification accuracy is assessed to recommend the most appropriate data combinations for such areas. The paper is divided into five sections. Section 1 is the introduction which highlights the previous work related to the use of LIDAR data in land cover information extraction. Section 2 comprises the methodology used in this research work. Section 3 describes the study areas and the datasets used. Section 4 includes the results of the experimental work and the analysis. The paper is concluded by a summary of the work and the future work in Section 5. 2. METHODOLOGY The work is conducted in two main steps; data preparation, and data classification and assessment. In the data preparation step, the point data recorded by the LiDAR sensor are converted into raster image data, prepared as bands, to be used for the classification step. The bands prepared are also combined and Principal Component Analysis (PCA) is used to produce principal component bands for more investigation. The second step is applying the classification algorithm on the different prepared datasets, and assessing the results. Four 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 information classes are identified in this study area. Details of the work procedure are discussed in the following sub-sections. 2.1 Data Preparation LiDAR Point Clouds (x, y, z, I) Data Separation Intensity points DSM points Terrain points (x y, 1) (x y, z) (x, y, z) | | Conversion to Raster Data Y Y Y Radiometric Digital Surface Digital Terrain Corrected Itenstty Model Image Model Image (RCI) Ds (DTM) Height Calculation Normal Heights (NH) Surface c Spatial Enhancement y Topographic Analysis Texture of Slope of DSM Slope of NH Intensity Data Data Data 1 2 3 Band Combination i i i i y l RCland DSM| |RCI, DSM and RCI, DSM, RCI and NH RCI, NH and RCI, NH, d Texture Texture and g Texture Texture and © Slope h Siope Principal Component Analysis (PCA) Principal Component Analysis (PCA) Principal Principal Component Component Bands Bands i k Figure 1: Work Flow of Data Preparation The data sets are prepared by converting the data collected by the sensor (range and intensity data) into raster image data. Since the multi-returned data are not available, the terrain has been separated manually from object surface by selecting the point data that falls on the roads and the terrain. The Kriging interpolation algorithm is used for point data conversion into image data. New image data (bands) are created representing the followings: i) DSM, ii) DTM, iii) Intensity, iv) Normal

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