Distributed water depth prediction method based on GWR (geographically weighted regression) and BP (back propagation) neural network

Abstract

The invention discloses a distributed water depth prediction method based on GWR (geographically weighted regression) and a BP (back propagation) neural network. The method comprises the following steps: firstly, preprocessing a remote sensing image; secondly, establishing sampling data based on an actually measured water depth value acquired by a laser radar and DN (digital number) values of blue and green wavebands of corresponding coordinate points in the remote sensing image; thirdly, dividing a to-be-predicted region into a plurality of sub-regions based on the GRW method and establishing a neural network water depth predication model of each sub-region; finally, designing weighting factors of the neural network water depth prediction models of all regions around each to-be-predicted point in the to-be-predicted region to establish a distributed neutral network water depth prediction model of the whole to-be-predicted region. Therefore, the distributed water depth prediction method is not influenced by seawater quality, seabed type or space diversity, can quickly and conveniently establish a nonlinear relationship between the multispectral remote sensing image and the actual water depth value, and has a very good practical value for prediction of shallow water depth.

Description

technical field [0001] The invention belongs to the technical field of shallow water depth remote sensing detection, and more specifically relates to a distributed water depth prediction method based on GWR and BP neural network. Background technique [0002] Bathymetry is an essential work in water conservancy, shipping, offshore engineering, water resource utilization, tidal flat development, etc. The traditional bathymetry method is to use the bathymetry equipment installed on the survey ship to measure the water depth of each point in the whole water area, and then calculate and draw the map according to the drawing requirements, so as to obtain the underwater topographic map of the measured water area. Due to the wide coverage of water depth information collection, the harsh environmental conditions in some areas, and the difficulty for sounding personnel to set foot in, there are many practical difficulties in this traditional sounding method. [0003] With the rapid ...

AI Technical Summary

Problems solved by technology

However, since the real marine environment is very complex, there may be various situations such as unbalanced water quality, inconsistent seabed materials, turbid water, and spatial diversity. Considering the influence of spatial diversity and geographic location changes, spatial non-stationarity cannot be eliminated, making the depth accuracy of the inversion not ideal

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