Lineament extraction method of remote sensing image

Abstract

The invention discloses a lineament extraction method of a remote sensing image, comprising the following steps of: (1) inputting a remote sensing image and carrying out the preprocessing on the image enhancement; (2) establishing a multi-resolution structural image sequence with a pyramid structure; (3) extracting lineaments in different levels for all levels of images with different resolutionsaccording to the consistency of gradient direction and the consistency of the regional gray; and (4) establishing characteristic database for the extracted lineaments and managing the extracted lineaments. In the method, by carrying out the pyramid classification on the remote sensing image and extracting different lineaments, the application range can be widened; by fusing the gradient directionof the lineaments with the gray linear characteristics, the precision for recognizing the linearity can be improved; the calculated quantity is reduced and the leaking detection and the false detection are overcome by using a fixed area while the lineaments are generated; and the lineament extraction method has favorable robustness and accuracy.

Description

technical field [0001] The invention relates to the technical fields of remote sensing technology and image processing, in particular to a method for extracting linear elements of high-resolution remote sensing images. Background technique [0002] Due to the high diversity and complexity of targets in remote sensing images, a successful automatic object recognition system will provide general guiding theories and methods for other types of image understanding problems. Therefore, how to identify and extract objects in remote sensing images is one of the important research topics in remote sensing image target recognition. Linear feature extraction is an important research content of image geometric content. First, most man-made objects are planar; second, the shape of most object objects is based on the underlying linear line representation. Count and classify the detected edge straight line segments to obtain different types of linear line segments, so as to realize the ...

AI Technical Summary

Problems solved by technology

Hough transform has serious defects: First, the texture area of ​​the image has a high edge density, which often leads to many false detections; at the same time, because the direction of the edge points is ignored, the algorithm obtains linear segmentation results in abnormal directions

Kahn, Kitchen, and Riseman improved the algorithm, making the line segment segmentation well positioned, but still did not solve the threshold problem (P.Kahn, L.Kitchen, and E.M.Riseman. Real-time feature extraction: A fast line finder for vision-guided robot navigation.Technical Report 87-57, COINS, 1987; P.Kahn, L.Kitchen, and E.M.Riseman.A fast line finder for visionguided robot navigation.IEEE Trans.Pattern Anal.Mach.Intell., 12(11):1098-1102, 1990.)

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