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A framework for land cover classification using discrete return lidar data: adopting pseudo-waveform and hierarchical segmentation
| Content Provider | NASA Technical Reports Server (NTRS) |
|---|---|
| Author | Crawford, Melba M. Pasolli, Edoardo Prasad, Saurabh Tilton, James C. Jung, Jinha |
| Copyright Year | 2014 |
| Description | Acquiring current, accurate land-use information is critical for monitoring and understanding the impact of anthropogenic activities on natural environments.Remote sensing technologies are of increasing importance because of their capability to acquire information for large areas in a timely manner, enabling decision makers to be more effective in complex environments. Although optical imagery has demonstrated to be successful for land cover classification, active sensors, such as light detection and ranging (LiDAR), have distinct capabilities that can be exploited to improve classification results. However, utilization of LiDAR data for land cover classification has not been fully exploited. Moreover, spatial-spectral classification has recently gained significant attention since classification accuracy can be improved by extracting additional information from the neighboring pixels. Although spatial information has been widely used for spectral data, less attention has been given to LiDARdata. In this work, a new framework for land cover classification using discrete return LiDAR data is proposed. Pseudo-waveforms are generated from the LiDAR data and processed by hierarchical segmentation. Spatial featuresare extracted in a region-based way using a new unsupervised strategy for multiple pruning of the segmentation hierarchy. The proposed framework is validated experimentally on a real dataset acquired in an urban area. Better classification results are exhibited by the proposed framework compared to the cases in which basic LiDAR products such as digital surface model and intensity image are used. Moreover, the proposed region-based feature extraction strategy results in improved classification accuracies in comparison with a more traditional window-based approach. |
| File Size | 1395141 |
| Page Count | 12 |
| File Format | |
| Alternate Webpage(s) | http://archive.org/details/NASA_NTRS_Archive_20150001289 |
| Archival Resource Key | ark:/13960/t46q6zg3k |
| Language | English |
| Publisher Date | 2014-02-01 |
| Access Restriction | Open |
| Subject Keyword | Light Detection & Ranging (lidar) Hierarchical Segmentation Classification Image Resolution Classifications Image Processing Imaging Techniques Waveforms Data Processing Pattern Recognition Data Reduction Land Use Computer Programs Image Analysis Algorithms Cities Optical Radar Ntrs Nasa Technical Reports Server (ntrs) Nasa Technical Reports Server Aerodynamics Aircraft Aerospace Engineering Aerospace Aeronautic Space Science |
| Content Type | Text |
| Resource Type | Article |
