Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 733-739, 2016
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B3/733/2016/
doi:10.5194/isprs-archives-XLI-B3-733-2016
 
10 Jun 2016
OBJECT BASED IMAGE ANALYSIS COMBINING HIGH SPATIAL RESOLUTION IMAGERY AND LASER POINT CLOUDS FOR URBAN LAND COVER
Xiaoliang Zou1,2,3, Guihua Zhao3, Jonathan Li2, Yuanxi Yang1,3, and Yong Fang1,3 1State Key Laboratory of Geo-Information Engineering, Xi’an, China 710054
2Department of Geography and Environmental Management, Faculty of Environment, University of Waterloo, Waterloo, ON, Canada N2L 3G1
3Xi’an Institute of Surveying and Mapping, Xi’an, China 710054
Keywords: Object Based Image Analysis (OBIA), Image Segmentation, Classification, Accuracy Assessment, nDSM, Parcel Map, TrueOrtho Photo, Lidar Point Clouds Abstract. With the rapid developments of the sensor technology, high spatial resolution imagery and airborne Lidar point clouds can be captured nowadays, which make classification, extraction, evaluation and analysis of a broad range of object features available. High resolution imagery, Lidar dataset and parcel map can be widely used for classification as information carriers. Therefore, refinement of objects classification is made possible for the urban land cover. The paper presents an approach to object based image analysis (OBIA) combing high spatial resolution imagery and airborne Lidar point clouds. The advanced workflow for urban land cover is designed with four components. Firstly, colour-infrared TrueOrtho photo and laser point clouds were pre-processed to derive the parcel map of water bodies and nDSM respectively. Secondly, image objects are created via multi-resolution image segmentation integrating scale parameter, the colour and shape properties with compactness criterion. Image can be subdivided into separate object regions. Thirdly, image objects classification is performed on the basis of segmentation and a rule set of knowledge decision tree. These objects imagery are classified into six classes such as water bodies, low vegetation/grass, tree, low building, high building and road. Finally, in order to assess the validity of the classification results for six classes, accuracy assessment is performed through comparing randomly distributed reference points of TrueOrtho imagery with the classification results, forming the confusion matrix and calculating overall accuracy and Kappa coefficient. The study area focuses on test site Vaihingen/Enz and a patch of test datasets comes from the benchmark of ISPRS WG III/4 test project. The classification results show higher overall accuracy for most types of urban land cover. Overall accuracy is 89.5% and Kappa coefficient equals to 0.865. The OBIA approach provides an effective and convenient way to combine high resolution imagery and Lidar ancillary data for classification of urban land cover.
Conference paper (PDF, 1287 KB)


Citation: Zou, X., Zhao, G., Li, J., Yang, Y., and Fang, Y.: OBJECT BASED IMAGE ANALYSIS COMBINING HIGH SPATIAL RESOLUTION IMAGERY AND LASER POINT CLOUDS FOR URBAN LAND COVER, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B3, 733-739, doi:10.5194/isprs-archives-XLI-B3-733-2016, 2016.

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