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Automatic land-water classification using multispectral airborne LiDAR data for near-shore and river environments
- Shaker, Ahmed, Yan, Wai Yeung, LaRocque, Paul E.
- ISPRS journal of photogrammetry and remote sensing 2019 v.152 pp. 94-108
- aerial photogrammetry, coastal zone management, coasts, data collection, georeferencing, lidar, models, monitoring, remote sensing, rivers, satellites, selection methods, Lake Ontario, Ontario
- Rapid mapping of near-shore and coastal regions has become an indispensable task for the local authority to serve the purpose of coastal management and post-disaster monitoring. Aerial photogrammetry and satellite remote sensing have been utilized to fulfill such a task in the last few decades. Airborne LiDAR can further compensate the drawbacks of these image capturing approaches as a result of the direct geo-referenced 3D point cloud. The recent introduction of multispectral airborne LiDAR, such as the Teledyne Optech Titan, can potentially enhance the capability of water mapping, minimize the involvement of manual intervention and reduce the use of supplementary information or ancillary data. This study demonstrates the use of multispectral airborne LiDAR data for automatic land-water classification under different coastal and inland river environments. Two automatic training data selection methods are proposed. The first method utilizes Gaussian mixture model (GMM) to split preliminarily the land and water region based on the elevation/intensity histogram, and the second method is developed based on the use of scan line intensity-elevation ratio (SLIER). Subsequently, various LiDAR-derived feature sets, particularly based on the multispectral LiDAR intensity, are constructed in order to serve as an input for the log-likelihood classification model. Two optional post-classification enhancements can be implemented to further adjust the misclassified data points. The proposed workflow was evaluated with four Optech Titan datasets collected for different near-shore and river environments that are located nearby Lake Ontario, Ontario, Canada. Our experimental work demonstrated that the multispectral LiDAR intensity data was capable of enhancing the classification capability, where an overall accuracy better than 96% was achieved in most of the cases.