Forest type identification method and device

Abstract

The invention provides a forest type identification method and device, and relates to the technical field of forest type classification and identification. The method comprises the following steps: obtaining an identifier of a to-be-identified area and a target time interval corresponding to the to-be-identified area, obtaining target remote sensing data of the to-be-identified area in the target time interval from a cloud platform, and analyzing and processing the target remote sensing data by using a random forest model to obtain a forest type of the to-be-identified area. Through the above steps, the target remote sensing data of the to-be-identified area can be acquired from the cloud platform, and the forest type of the to-be-identified area can be identified. Therefore, the problems that in the prior art, when stand-alone software is adopted to apply a machine learning algorithm to recognize the type of a large-area forest through remote sensing data, a large amount of remote sensing data is preprocessed, the operation classification speed of the machine learning algorithm is low, and the storage cost of TB-level remote sensing image data is high are solved.

Description

technical field [0001] The present application relates to the technical field of forest type classification and identification, in particular, to a forest type identification method and device. Background technique [0002] Classifying and identifying forest types is an important part of forest resources management and monitoring. As an advanced means of earth observation, remote sensing technology provides a new way of thinking for the identification of forest types. Remote sensing data has been widely used in forest resource surveys due to its advantages of wide coverage, strong timeliness, low cost, and repeatable acquisition. [0003] In the prior art, stand-alone software can be used to apply machine learning algorithms to identify forest types using remote sensing data. However, when using the methods of the prior art to identify forests, especially large-scale forests, there are a large number of remote sensing data preprocessing, machine learning algorithms run at ...

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Problems solved by technology

[0004] In view of this, the purpose of this application is to provide a forest type classification method and device for the above-mentioned deficiencies in the prior art, so as to solve the problem of a large amount of remote sensing data in the prior art when performing type identification on large-area forests. Preprocessing, machine learning algorithm running and classification are slow and the storage cost of terabyte level remote sensing image data is high

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