High-resolution remote sensing image-oriented segmentation method

Abstract

The invention provides a high-resolution remote sensing image-oriented segmentation method. The method comprises the following steps of: inputting an original high-resolution remote sensing image; extracting a plurality of features of the image and forming a comprehensive feature vector; processing the comprehensive feature vector by utilizing a support vector data description method; and forming an image segmentation result. The method provided by the invention is mainly used for solving the problems of long time and low precision caused by multiple features and high resolution in the existing remote sensing image segmentation technology.

Description

Technical field [0001] The invention relates to the field of intelligent information processing, in particular to a segmentation method for high-resolution remote sensing images. Background technique [0002] Because high-resolution remote sensing images contain richer spatial information, they have become one of the hotspots of remote sensing technology research in recent years, but the rich information they contain also puts forward higher requirements for processing technology; because they cannot make full use of their wealth Containing information, traditional separate spectrum-based segmentation techniques often have the phenomenon of the same spectrum and different spectrum of the same substance. In addition, the traditional segmentation method often leads to longer training time and more Poor segmentation effect; At present, how to make full use of various information of high-resolution remote sensing images to achieve satisfactory segmentation results is still a challeng...

AI Technical Summary

Problems solved by technology

[0002] High-resolution remote sensing images have become one of the hotspots of remote sensing technology research in recent years because they contain richer spatial information, but the rich information they contain also put forward higher requirements for processing technology; The information contained in the traditional spectrum-based segmentation technology alone tends to have the phenomenon of different objects with the same spectrum and the same object with different spectra. In addition, the traditional segmentation method often leads to longer training time and more complex images when dealing with large-scale growing pixels. Poor segmentation effect; at present, how to make full use of various information of high-resolution remote sensing images to achieve satisfactory segmentation effect is still a challenging research topic

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