Vegetation index idea-based ground object information remote sensing extraction method

Abstract

The present invention belongs to the remote sensing classification technical field and relates to a vegetation index idea-based ground object information remote sensing extraction method. The method includes the following steps that: S1, a ground object target is selected, corresponding NEW-NDVI or NEW-RVI values are calculated; S2, parts in the NEW-NDVI or NEW-RVI values obtained in the step 1, which are larger than T, are extracted through applying a threshold segmentation technique, namely, a part of non-ground object information can be eliminated; and S3, set intersection operation in the mathematics is adopted to perform intersection on the obtained NEW-NDVI or NEW-RVI values, so that final target ground object information can be obtained. According to the vegetation index idea-based ground object information remote sensing extraction method of the invention, a target ground object feature information curve is made to be equivalent to a chlorophyll spectrum characteristic curve, so that target information can be extracted, and therefore, the method is stable in extraction precision, high in universality, high in automation degree, is suitable for various environments and can assist in avoiding the interference of man-made factors in an extraction process. Information extracted by using the method is accurate. The extraction method has simplicity and is of great significance for mastering ground object target spatial distribution information and promoting the application and development of remote sensing classification technologies.

Description

technical field [0001] The invention relates to the technical field of remote sensing classification, in particular to a remote sensing extraction method of ground object information based on the idea of ​​vegetation index. Background technique [0002] Feature recognition and extraction have important application significance in many fields of society. Remote sensing is a large-scale, rapid acquisition of surface features information technology. Therefore, the technology and method of timely and accurately obtaining the spatial distribution information of ground objects is one of the important research directions in the field of remote sensing. At present, there are many remote sensing extraction techniques and methods for ground objects, such as extraction methods based on different classification techniques, including supervised classification and unsupervised classification (such as visual interpretation, decision tree, neural network, support vector machine, aggregatio...

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Problems solved by technology

[0003] However, according to the analysis of existing research results, it is found that the existing remote sensing extraction methods of ground objects have the following problems: first, the extraction accuracy is unstable, especially the spatial position accuracy is low; second, the method has regional limitations and poor universality; third, it needs Manually collect a large number of training samples of ground objects; fourth, the interference of human factors is relatively large; fifth, the method is complicated; sixth, the degree of automation is low

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