Paper
24 October 2011 Neural network modeling of tidal flat terrain based on lidar survey data
Qing Li, Xianrong Ding, Ang Zhu, Ligang Cheng, Yanyan Kang, Tingting Zhang
Author Affiliations +
Proceedings Volume 8286, International Symposium on Lidar and Radar Mapping 2011: Technologies and Applications; 828625 (2011) https://doi.org/10.1117/12.913031
Event: International Symposium on Lidar and Radar Mapping Technologies, 2011, Nanjing, China
Abstract
The southern yellow sea radial submarine sand ridges are in the central Jiangsu coast, where sediment dynamics is complex and the tidal ridges and channels are changing. The purpose of this paper is to model tidal flat terrain. Based on the regularity and variability characteristics of the tidal flats combined with remote sensing and LiDAR survey data, this research focuses on tidal flat terrain modeling with a neural network method. Firstly, the network structure and the parameters involved, such as weights and offset values of neurons, are determined by the BP Neural Network calculation using the 2006 LiDAR DEM in this area. Secondly, the characteristic lines, which are boundary lines of tidal basins, skeleton lines of tidal creeks and a series of waterlines, and so on are extracted from TM images of the no-data region similar to the area of study. Combining with survey data, the elevation data of characteristic lines are obtained. At last, the terrain of the region without elevation data is generated by the model. The test shows the terrain calculated by the model is very close to the surveyed terrain. The residual distribution is normal. The study is significant in getting a dynamic tidal flat terrain fast and efficiently.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Qing Li, Xianrong Ding, Ang Zhu, Ligang Cheng, Yanyan Kang, and Tingting Zhang "Neural network modeling of tidal flat terrain based on lidar survey data", Proc. SPIE 8286, International Symposium on Lidar and Radar Mapping 2011: Technologies and Applications, 828625 (24 October 2011); https://doi.org/10.1117/12.913031
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Data modeling

Neural networks

LIDAR

Neurons

Remote sensing

Statistical modeling

Calibration

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