Multi-angle remote sensing image forest height extraction method based on convolutional neural network

Abstract

The invention discloses a multi-angle remote sensing image forest height extraction method based on a convolutional neural network. The multi-angle remote sensing image forest height extraction methodsequentially comprises the following steps: performing ortho-rectification and resampling on a resource No.3 multi-angle remote sensing image; extracting forest height based on laser radar data, andrecording latitude and longitude coordinates of corresponding light spots; cutting the multi-angle remote sensing image by taking the light spot coordinate as a center, and making a training sample set; constructing a convolutional neural network, training the network and storing the model; cutting the multi-angle remote sensing image in a sliding cutting mode; and extracting the stored model prediction forest height, and making a forest height distribution map based on the research area. A new idea is provided for scale extrapolation of the forest height, programming implementation is easy, the operation efficiency is high, the generalization capacity is high, and the generated forest height distribution map shows good regional consistency.

Description

technical field [0001] The invention relates to a method for extracting forest height from multi-angle remote sensing images based on convolutional neural network, which belongs to the field of deep learning and forestry. The invention has strong generalization and feasibility, and can be used to realize forest height mapping in continuous regions researching. Background technique [0002] Forest height is an important feature that reflects the vertical structure of forests. It is crucial to the study of carbon cycle and plays an irreplaceable role in the estimation of forest biomass and the study of dynamic changes. The traditional forest height survey adopts the method of sampling survey and manual measurement, which is time-consuming and laborious and difficult to obtain. The emergence of remote sensing technology has greatly made up for the shortcomings of traditional surveys. Lidar technology, as an emerging technology, provides the possibility to accurately measure th...

AI Technical Summary

Problems solved by technology

The traditional forest height survey adopts the method of sampling survey and manual measurement, which is time-consuming and laborious and difficult to obtain

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