Improved Urban Scene Classification Using Full-Waveform Lidar
Full-waveform lidar data provides supplementary radiometric as well as more accurate geometric target information, when compared to discrete return systems. In this research, a wide range of classes in an urban scene; including trees, medium vegetation, low vegetation (grass), water bodies, pitched roofs, flat roofs, asphalt, vehicles, power lines, walls (fences) and concrete are considered. In order to tackle the challenge of distinguishing geometrically similar classes and enhancing the separability of other targets, a new set of features based on deconvolved waveforms is introduced. The positive effect of the proposed feature dataset on classification accuracy in individual classes is shown using two ensemble classifiers (random forests and RUSBoost). Performance of the classifiers is improved by integration with sampling techniques, especially for the under-represented classes. The final output of the proposed method is a highly detailed land cover map of the urban scene, which affords good separability between critical classes.
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Document Type: Research Article
Publication date: December 1, 2016
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- The official journal of the American Society for Photogrammetry and Remote Sensing - the Imaging and Geospatial Information Society (ASPRS). This highly respected publication covers all facets of photogrammetry and remote sensing methods and technologies.
Founded in 1934, the American Society for Photogrammetry and Remote Sensing (ASPRS) is a scientific association serving over 7,000 professional members around the world. Our mission is to advance knowledge and improve understanding of mapping sciences to promote the responsible applications of photogrammetry, remote sensing, geographic information systems (GIS), and supporting technologies.
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