Urban tree canopy detection using object-based image analysis for very high resolution satellite images: A literature review

摘要

Urban Tree Canopy (UTC) is the layer of leaves, branches and stems of trees that cover the ground when viewed from above. Remotely sensed data have played an important role in detecting urban morphologies effectively. Remote sensing datasets contain more information of earth surface as compared with usual urban maps hence used in urban planning and management. Very high resolution (VHR) satellite imageries provide resolution less than 1m. These imageries have enhanced the applications of remote sensing. Timely and accurate information of urban land cover and biophysical parameters is crucial. Earth observations which are being useable play an important role in detecting, management and solving environmental problems such as climate changes, deforestation, disasters, land use, water resource and carbon cycle. With the help of high resolution satellite imageries it is possible to get the details on earth surface. A different approach is used to get the efficient result called Object-based image analysis (OBIA) in which the image is divided into homogeneous regions prior to classification instead of classifying individual pixels. These are called segments, or image objects. OBIA have gain popularity as a method bridging the gap between the increasing amount of detailed geospatial data and the inefficient results of conventional pixel base classifiers.