Object-based classification and building extraction by integrating airborne LiDAR data and aerial image
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It is generally difficult to classify an object type having different colors into the same class using only optical data such as a satellite or aerial image. This paper proposes a method that solves this problem by combining LiDAR data and an aerial image. The method extracts building pixels from LiDAR data and then identifies building objects on the aerial image by overlaying the LiDAR result to a segmented aerial image through the definite rule. This process plays a role in transforming building objects of LiDAR data to ones of the aerial image.
[1] Young-Gi Byun,et al. A framework for the segmentation of high-resolution satellite imagery using modified seeded-region growing and region merging , 2011 .
[2] Li Zhang,et al. An object-based two-stage method for a detailed classification of urban landscape components by integrating airborne LiDAR and color infrared image data: A case study of downtown Houston , 2009, 2009 Joint Urban Remote Sensing Event.
[3] Stuart Barr,et al. Reducing structural clutter in land cover classifications of high spatial resolution remotely-sensed images for urban land use mapping , 2000 .