Multiscale hierarchical processing method for extracting object-oriented high-spatial resolution remote sensing information

Abstract

The invention discloses a multiscale hierarchical processing method for extracting object-oriented high-spatial resolution remote sensing information. The method comprises the following steps: at the global level, determining a texture sampling interval and a template window size by utilizing image semi-variance statistical calculation, and dividing the images into several local areas with smooth textures in a course scale on the basis of image texture characteristics; at the local level, setting local segmentation scale parameters through space statistical calculation by taking the local areas as units, and carrying out fine segmentation by utilizing the geometric and spectral information of the images so as to obtain refined image objects which are capable of embodying more details; and carrying out object-oriented remote sensing image classification by taking the global or local areas as units through utilizing a global or local area sample training classifier. According to the method provided by the invention, multiscale hierarchical processing is carried out, so that both the macro and micro characteristics of the images are considered and the ground features can be divided more accurately; and the specific characteristics of the remote sensing images are combined to decide whether to partition to carry out image classification, so that the classification accuracy of the whole image is improved.

Description

technical field [0001] The invention relates to the field of remote sensing geoscience analysis methods, in particular to an object-oriented multi-scale layered processing method for extracting remote sensing information with high spatial resolution. Background technique [0002] High-resolution remote sensing images have a large amount of data, complex details and scale dependence. Combined with the object-oriented image analysis method of multi-scale segmentation, it can comprehensively consider the spectrum, shape, texture and other characteristics of remote sensing images, and can express the information of high-resolution remote sensing images in a more comprehensive and multi-level manner. Therefore, the object-oriented remote sensing information extraction method is more are getting more and more attention. The accuracy of object-oriented remote sensing information extraction is closely related to image segmentation and image classification algorithms. [0003] Imag...

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Problems solved by technology

[0009] The problem solved by the present invention is: the idea of ​​multi-scale layered processing is adopted to solve the contradiction that the macroscopic and microscopic information cannot be taken into account simultaneously in the traditional remote sensing image interpretation (image segmentation process) to a certain extent; in the layered processing process, based on The theoretical method of spatial statistics, quantitatively setting the theoretical optimal parameters of the relevant scale processing

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