Study on Remote Sensing of Water Depth Extraction Based on Artificial Neural Networks

被引：0

作者：

Zhang, Zhenxing ^{[1
]}

Hao, Yanling ^{[1
]}

机构：

[1] Harbin Engn Univ, Coll Automat, Harbin, Heilongjiang, Peoples R China

来源：

EPLWW3S 2011: 2011 INTERNATIONAL CONFERENCE ON ECOLOGICAL PROTECTION OF LAKES-WETLANDS-WATERSHED AND APPLICATION OF 3S TECHNOLOGY, VOL 2 | 2011年

关键词：

remote sensing; depth extraction; neural network; multispectral;

D O I：

暂无

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

The water depth extraction of shallow reefs plays an important role for the shipping, marine engineering and marine safety. The IKONOS satellite remote sensing image and practical measured depth data are analyzed and processed in this paper, and the neural network inversion model is established. The blue, green, red and near-infrared bands of IKONOS are used to calculate the water depth. The main advantage of neural network inversion model is its fast extraction by using the remote sensing image data directly, without regard to the other environmental factors (such as sea sediments, marine organisms, etc.). The model can establish the non-linear relationship between the multi-spectral IKONOS data and the practical measured depth data. It is more reliable than traditional linear regression modal.

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页码：586 / 589

页数：4