Geological Linear Body Extraction via Tensor Voting and Hough Transform
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Solution Overview
Problem
Existing methods for extracting geological linear bodies from remote sensing images rely heavily on expert knowledge and are time-consuming, inefficient, and prone to noise, particularly when processing large images with high resolution, which limits their universality and accuracy.
Innovation Solution
A geological linear body extraction method based on tensor voting coupled with Hough transformation, involving pre-processing, waveband selection, Gaussian high-pass filtering, edge detection using tensor matrices, and conversion to a parameter coordinate system to enhance edge detection and reduce reliance on expert knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert knowledge and experience are used for visual interpretation, then extraction accuracy may be improved, but processing time and labor consumption increase significantly
Solution Approach 1:
The patent replaces the manual visual interpretation mechanism (expert knowledge and experience) with an automated computer-based mechanism (tensor voting algorithm coupled with Hough transformation). This substitution eliminates the need for human experts to manually analyze remote sensing images, thereby significantly reducing processing time and labor consumption while maintaining extraction accuracy through the sophisticated automated algorithms.
2Measurement precision
If high resolution data is used, then extraction accuracy is improved, but processing speed decreases and noise increases
Solution Approach 1:
The patent extracts and emphasizes linear feature information from remote sensing images through the tensor voting mechanism, which specifically detects and enhances linear patterns while suppressing non-linear noise. The coupled Hough transformation then extracts linear body parameters from these enhanced features. This selective extraction process maintains high extraction accuracy while reducing the impact of high-resolution noise and improving processing efficiency.
3Productivity
If traditional edge detection methods are used, then processing speed is maintained, but extraction accuracy and robustness decrease due to noise
Solution Approach 1:
The patent creates a composite processing mechanism by coupling two different algorithms: tensor voting and Hough transformation. The tensor voting part provides robust edge detection with noise resistance, while the Hough transformation part ensures accurate linear parameter extraction. This composite approach maintains processing speed while significantly improving extraction accuracy and robustness against noise compared to traditional single-method approaches.
Data Source
AI summary
The present disclosure provides geological linear body extraction method based on tensor voting coupled with Hough transformation, including pre-processing a remote sensing image to obtain a pre-processed remote sensing image; selecting three optimal wavebands from N multi-spectral wavebands of the pre-processed remote sensing image, so as to obtain a remote sensing image combined by the optimal wavebands, N being a natural number greater than or equal to 3; using Gaussian high-pass filtering to perform sharpening processing on the remote sensing image combined by the optimal wavebands, so as to enhance linearized edge information; performing edge detection on the remote sensing image having enhanced linearized edge information, so as to obtain all edge points in the remote sensing image; and converting all the edge points in the remote sensing image from an image coordinate system to a parameter coordinate system, and extracting a geological linear body from the parameter coordinate system.