High-resolution remote sensing image segmentation method combining phase and spectrum

Abstract

The invention discloses a high-resolution remote sensing image segmentation method combining a phase and a spectrum. For the limitation existing in the aspects of weak edge detection and the like dueto the fact that gradient information is excessively depended during segmentation of a traditional method, advantage complementation is conducted by introducing phase information, and firstly, phase consistency information is extracted through Log Gabor filtering; on the basis, a local homogeneity index J-value in a traditional JSEG algorithm is adopted to optimize an edge detection result, and anobjective function optimization strategy based on inter-scale mutual information minimization is provided, so that a parameter adaptive optimal parameter combination and response model is constructed, and finally, the multi-scale region segmentation and region merging is carried out based on the model to obtain a final segmentation result.

Description

technical field [0001] The invention relates to a high-resolution remote sensing image segmentation method combining phase and spectrum, and belongs to the technical field of remote sensing image processing. Background technique [0002] With the continuous improvement of the spatial resolution of remote sensing images, the spectral differences within the same ground objects are also more significant, making it increasingly difficult for traditional pixel-level analysis methods to meet the needs of practical applications. Object-Based Image Analysis (OBIA) technology is the basic analysis unit of Geographical object, which provides a new idea for information extraction of high-resolution remote sensing images. Carrying out OBIA first requires effective image segmentation, which is also the basic premise and important guarantee for accurate feature extraction and target recognition in subsequent processing. [0003] Compared with medium- and low-resolution remote sensing ima...

AI Technical Summary

Problems solved by technology

However, these methods rely too much on the gradient information of the image, and the response to the low-contrast part of the image is weak, which easily leads to the loss of contour information.

On the other hand, building an edge detection model based on local homogeneity information can not only reduce the dependence on gradient information, but also be very sensitive to weak edges, but at the same time, the segmentation process is more significantly disturbed by noise and false edges.

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