Watershed segmentation mark point extraction method based on local area homogeneity indexes

Abstract

The invention discloses a watershed segmentation mark point extraction method based on local area homogeneity indexes. Firstly, an original image is quantized to obtain a quantized image, and a J-image is formed with the J-value of each pixel serving as the pixel value of the pixel; the J-image is defined, the original image is segmented, the texture complexity of each region is judged, and therefore distribution of mark points can be restrained through different threshold values. By means of the method, image detail loss caused by pre-filtering processing is avoided, more mark points are extracted in the regions with the complex texture so as to extract the detail features of the image, fewer mark points are adopted in the regions with the homogeneous texture so as to avoid over-segmentation, and therefore reasonable restraint on mark point distribution is achieved.

Description

technical field [0001] The invention relates to a watershed segmentation mark point extraction method based on a local homogeneity index. On the basis of the traditional watershed segmentation based on a mark point, the self-adaptive extraction and distribution constraints of a mark point are realized by introducing a local homogeneity index, which belongs to remote sensing Image processing technology field Background technique [0002] In the past 30 years, remote sensing images have been widely used in various fields of social life such as land resource planning and natural disaster monitoring due to their characteristics of multi-band, numerous types of ground objects, rich texture features, multi-scale, and wide coverage. Among the many related technologies involved in the field of remote sensing image processing, remote sensing image segmentation realizes the extraction of objects with physical meaning in the scene, which is the basis for further image classification an...

AI Technical Summary

Problems solved by technology

However, the watershed algorithm usually needs to filter the interference factors such as isolated points and noise before image segmentation, which is easy to cause the loss of detailed information in the image; the marker point extraction strategy based on a single threshold is difficult to accurately mark various images with different shapes in the scene. , size and texture complexity, so it is easy to produce over-segmentation and under-segmentation in the segmentation results

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