Name
Title | ||
|---|---|---|
A Framework for Land Cover Classification Using Discrete Return LiDAR Data: Adopting Pseudo-Waveform and Hierarchical Segmentation |
Abstract | ||
|---|---|---|
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 LiDAR data. 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 features are 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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/JSTARS.2013.2292032 | Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of |
Keywords | Field | DocType |
geophysical image processing,image classification,image segmentation,land cover,optical radar,remote sensing by laser beam,anthropogenic activities,basic lidar products,digital surface model,discrete return lidar data,hierarchical segmentation,intensity image,land cover classiflcation,land-use information,natural environments,neighboring pixels,optical imagery,pseudowaveform segmentation,region-based feature extraction strategy,remote sensing technologies,spatial-spectral classification,traditional window-based approach,unsupervised strategy,classification,hierarchical segmentation (hseg),light detection and ranging (lidar),pseudo-waveform,support vector machine (svm),data processing,image analysis,data reduction,algorithms,waveforms,image resolution,image processing,pattern recognition,land use | Spatial analysis,Data mining,Data processing,Remote sensing,Image segmentation,Lidar,Artificial intelligence,Contextual image classification,Land cover,Computer vision,Segmentation,Feature extraction,Mathematics | Journal |
Volume | Issue | ISSN |
7 | 2 | 1939-1404 |
Citations | PageRank | References |
8 | 0.55 | 27 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jin-Ha Jung | 1 | 66 | 5.42 |
| Edoardo Pasolli | 2 | 285 | 17.04 |
| Saurabh Prasad | 3 | 860 | 58.52 |
| James C. Tilton | 4 | 489 | 34.22 |
| Melba M. Crawford | 5 | 1311 | 83.56 |