Method for automatically recognizing taxus cuspidata in high-resolution remote sensing images

Abstract

The invention provides a method for automatically recognizing taxus cuspidata in high-resolution remote sensing images. The method has the advantages that only few ground samples are needed for high-accuracy recognition of the taxus cuspidata in the high-resolution remote sensing images, the advantages of low cost and wide coverage of the remote sensing images are fully utilized, the taxus cuspidata can be recognized from the remote sensing images on the basis of the few samples according to its unique features, high recognition quality is acquired, and accordingly, high social and economic value is achieved.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image analysis and processing, and in particular relates to a method for automatically identifying Northeast yew in high-resolution remote sensing images. Background technique [0002] Northeast yew is a species of endangered plants under national first-class protection. It is a precious tree species left over from the tertiary period. It has a history of 2.5 million years and has high research value; a variety of anti-cancer substances can be extracted from the Northeast yew, which is the most promising anti-cancer drug in the world. one of the cancer drugs. Most of these tree species are distributed in Laoyeling, Zhangguangcailing and Changbai Mountains in Jilin Province. Locating and identifying the location of these trees will help protect and make full use of this unique species, which has high ecological, social and economic values. [0003] In the current application and research, ...

AI Technical Summary

Problems solved by technology

[0003] In the current application and research, there are two ways to find the Northeast yew in an area: one is to find the tree species through field investigation. Since the Northeast yew is distributed in mountainous areas with an altitude of 500-1000 meters, such areas are vast and most It is difficult to reach, the cost is high and the efficiency is low through on-the-spot investigation; the second is to use the supervised classification algorithm to identify the Northeast yew in high-resolution remote sensing images, and the content of remote sensing images captured by satellites can cover a large area. On the basis of learning a large number of ground target samples, supervised classification algorithms such as Convolutional Neural Networks can identify targets in remote sensing images quickly and at low cost. It is difficult to obtain a sufficient number of ground samples of Northeastern yew to train the algorithm. On the other hand, since most recognition algorithms have the characteristics of tolerable scale or color changes, it is difficult to distinguish the Northeastern yew from other mixed plants, resulting in supervision. Classification Algorithms Difficult to Work

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