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.
Highlights
With the rapid developments of the sensor technology, high spatial resolution imagery and Light Detection And Ranging (Lidar) cloud points can be captured nowadays, which makes classification, extraction, evaluation and analysis of a broad range of object features available
The evaluation method is to use these known reference points to assess the validity of the classification results, to compute a confusion matrix based on errors of omission and commission, and to derive the user’s accuracy and producer’s accuracy for each class, an overall accuracy as well as kappa coefficient
Images objects are generated through multi-resolution segmentation using the different weighting of image layer from high spatial resolution imagery, ancillary normalised digital surface model (nDSM) data and thematic data
Summary
With the rapid developments of the sensor technology, high spatial resolution imagery and Light Detection And Ranging (Lidar) cloud points can be captured nowadays, which makes classification, extraction, evaluation and analysis of a broad range of object features available. The combination of high resolution imagery and Lidar point clouds based on Object Based Image Analysis (OBIA) method can be produced results at greater classified accuracy than the contributions of either field alone. Many researchers have been attempting to combine multi-data to identify or extract object feature, to detect elevated objects such as building and tree, to detect changes of vegetation or to estimate for vegetation parameters, and to classify or segment properties in land cover and land use. Classification accuracy can be improved by accessing to accurate ancillary information and other types of modelling such as DEM, DSM or Lidar point clouds. The concepts of pixel-based image processing were developed in the 1970s and pixel-based image
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