CROSS REFERENCE TO RELATED APPLICATIONS

This patent application is a National Stage Entry of PCT/CN2018/105966 filed on Sep. 17, 2018, which claims the benefit and priority of Chinese Patent Application No. 201810121330.4 filed on Feb. 7, 2018, the disclosures of which are incorporated by reference herein in their entirety as part of the present application.

BACKGROUND

The present disclosure relates to the field of remote sensing geology and image processing technologies, and more particularly, to a geological linear body extraction method based on tensor voting coupled with Hough transformation.

Geological structures such as fault regions and faults belong to geologically weak regions, and they may form linear landforms due to erosion effects and the like. Under the action of the geological structures, these linear landforms generally present obvious linear distribution on remote sensing images, which are referred to as geological linear bodies. These geological linear bodies control the migration of underground fluids (ore solutions, groundwater, and oil and gas, etc.) and spatial occurrence of mineral resources. Orientations and densities of the underground fluids and mineral resources have far-reaching scientific significance and practical value for analyzing regional tectonic movement trends and activity levels.

Since the 1980s, many domestic and overseas remote sensing geologists have used expert knowledge and experience and image processing methods to extract geological linear bodies of remote sensing images and digital elevation models (DEM), and analyze regional geological structure trends and effect degree. For example, Jansson et al. (2005) used Landsat 7 ETM+ and digital elevation models to extract geological linear bodies and mapped glacial landforms in the northeast of Wales. Wu Jing et al. (2011) extracted fault structure information using Canny edge detection and Hough transformation, and used ENVI+IDL and other programming languages. Yuan Xiaoxiang et al. (2011) highlighted the contrast of geological linear bodies on multi-source remote sensing data using false color synthesis, principal component transform, tasseled cap transformation, waveband ratio, landform rendering and the like, and extracted active faults. Alaa, A M et al. (2011) extracted geological linear bodies on images processed by mountain shadow rendering using a clue tracing algorithm based on DEM data and linear features of mountain shadow enhancement, used a B-spline curve to provide an integrated expression, and finally evaluated regional geological structural environment conditions. Yusof et al. (2011) analyzed the relationship between landslide hazard distribution around highways and geological linear body density. Liu Zhirong et al. (2012) extracted and analyzed information and distribution of active faults in Yinchuan using image enhancement processing algorithms such as contrast enhancement, color synthesis, directional filtering and image fusion. Bahiru et al. (2016) extracted and mapped the geological linear structure distribution in Uganda using Landsat ETM+ and SRTMDEM data, studied the distribution of gold mines in this region, which had an important research significance for mineral prediction.

Some achievements have been achieved in different degrees in the above researches. However, there mainly are the following deficiencies.

1) The correctness of visual interpretation results relies on experience and knowledge backgrounds of interpretation experts, which is time-consuming, labor-consuming, and inefficient.

2) The accuracy of computer interpretation is related to the processing speed and the resolution of the data source. The larger an image is, the slower the processing speed is. If the resolution is too high, the image is easily affected by linear features such as roads and land use boundaries, which may produce a large number of wrong linear edges, and thus producing too much noise. Relying too much on parameter settings may lead to poor general universality.

BRIEF DESCRIPTION

In view of deficiencies of existing technologies, embodiments of the present disclosure provide a geological linear body extraction method based on tensor voting coupled with Hough transformation, so as to solve the problem that the existing technologies rely on experience and knowledge of interpretation experts, and are time and labor consuming, low in efficiency, slow in processing speed, high in noise, and poor in universality.

To solve the above technical problems, the present disclosure is implemented by using the following technical solutions.

A geological linear body extraction method based on tensor voting coupled with Hough transformation includes following steps:

Step1, pre-processing a remote sensing image to obtain a pre-processed remote sensing image;

Step2, selecting three optimal wavebands from N multi-spectral wavebands of the pre-processed remote sensing image to obtain a remote sensing image combined by the optimal wavebands, N being a natural number greater than or equal to 3;

Step3, performing sharpening processing on the remote sensing image combined by the optimal wavebands by using Gaussian high-pass filtering to enhance linearized edge information;

Step4, performing edge detection on the remote sensing image having enhanced linearized edge information in Step3to obtain all edge points in the remote sensing image; and

Step5, converting all the edge points in the remote sensing image from an image coordinate system to a parameter coordinate system to extract a geological linear body from the parameter coordinate system.

The image coordinate system uses any corner of the remote sensing image as an origin, a horizontal direction of the remote sensing image as an x-axis, and a vertical direction of the remote sensing image as a y-axis.

The parameter coordinate system is expressed as ρ=x cos θ+y sin θ, where θ and ρ represent polar coordinates of the edge points in the parameter coordinate system respectively.

Further, pre-processing a remote sensing image in step1includes selecting any remote sensing image from a remote sensing image database as a current remote sensing image, the current remote sensing image containing a cloud amount of less than 5%, and having rational polynomial coefficients (RPC) and statistical image grayscale information, the image grayscale information including a gray variance and a standard deviation for each waveband, and performing radiometric calibration, atmospheric correction, and image cropping on the current remote sensing image.

Further, selecting three optimal wavebands from N multi-spectral wavebands of the pre-processed remote sensing image in step2includes:

selecting three wavebands corresponding to a maximum optimum index factor (OIF) from the multi-spectral wavebands of the pre-processed remote sensing image as the optimal wavebands;

wherein the OIF is obtained based on Formula (1) as below:

O⁢⁢I⁢⁢F=∑i=13⁢si∑j=13⁢Ri⁢j(1)

where in Formula (1), Sirepresents a standard deviation of an ith waveband, Rijrepresents a correlation coefficient between the ith waveband and a jth waveband, i≠j, i=1, 2, 3, j=1, 2, 3; and

Ri⁢j=C⁢o⁢v⁡(i,j)D⁡(i)⁢D⁡(j),
where D(i) and D(j) respectively represent a variance of the ith waveband and a variance of the jth waveband, and Cov(i,j) represents a covariance between the ith waveband and the jth waveband.

Further, performing sharpening processing on the remote sensing image combined by the optimal wavebands by using Gaussian high-pass filtering in step3includes:

performing Gaussian high-pass filtering on the remote sensing image combined by the optimal wavebands to obtain a filtered remote sensing image H(u,v) based on Formula (2) as below:

H⁡(u,v)=1-e-D2⁡(u,v)2⁢D02(2)

where, in Formula (2), D(u,v) represents a distance between a frequency domain midpoint (u,v) of the remote sensing image combined by the optimal wavebands and a frequency rectangle center, and D0represents a constant.

Further, performing edge detection on the remote sensing image having enhanced linearized edge information in Step3to obtain all edge points in the remote sensing image in step4includes:

Step41: arbitrarily selecting a pixel from the remote sensing image having enhanced linearized edge information as a current pixel point, and performing Laplacian convolution on the current pixel to obtain a tensor matrix T;

where

T=[∂2⁢I∂x2∂2⁢I∂x⁢∂y∂2⁢I∂y⁢∂x∂2⁢I∂y2],
I represents the remote sensing image having enhanced linearized edge information, and

∂2⁢I∂x2
and

∂2⁢I∂y2
respectively represent a second derivative of the remote sensing image I along a direction x and a second derivative of the remote sensing image I along a direction y, and

∂2⁢I∂x⁢∂y
and

∂2⁢I∂y⁢∂x
respectively are second derivatives of the remote sensing image I along the x and y directions and along the y and x directions, wherein x and y respectively represent an x-axis and ay-axis of an image coordinate system established by taking any corner of the remote sensing image I as an origin, a horizontal direction of the remote sensing image I as the x-axis, and a vertical direction of the remote sensing image I as the y-axis.

Step42: performing matrices spectrum decomposition on the tensor matrix T to obtain a rod-shaped component and a spherical component;

where

T=(λ1-λ2)⁢e→1⁢e→1T+λ2⁡(e→1⁢e→1T+e→2⁢e→2T),⁢e→1⁢e→1T
represents the rod-shaped component, and

(e→1⁢e→1T+e→2⁢e→2T)
represents the spherical component;

Step43: determining the current pixel as an edge point, if (λ1-λ2)>λ2, otherwise, determining the current pixel as a non-edge point; and

Step44: repeating the Step41to Step43until all pixels of the remote sensing image having enhanced linearized edge information are determined as current pixels to obtain all the edge points in the remote sensing image.

Further, converting all the edge points in the remote sensing image from an image coordinate system to a parameter coordinate system to extract a geological linear body from the parameter coordinate system in step5includes:

Step51, traversing the parameter coordinate system to search for a point with a local maximum value, determining the point with the local maximum value as a peak point, and setting coordinates of the peak point as (ρ, θ), where (ρ, θ) respectively represent a slope and an intercept of the geological linear body in the remote sensing image; and

Step52: converting the coordinates corresponding to the peak point in the parameter coordinate system into the image coordinate system, and connecting the edge points according to the direction of the edge points and distance of the endpoint to obtain an image of the geological linear body, thereby completing the extraction of the geological linear body. Compared with the existing technologies, the present disclosure has the following technical effects.

1. Combining algorithms and rules such as waveband selection, image enhancement, boundary detection, and linear extraction, the present disclosure provides a geological linear body extraction method based on tensor voting coupled with Hough transformation. Compared with simple visual interpretation methods, this method relies less on knowledge and experience of interpretation experts, thereby considerably shortening the time of processing, saving a vast amount of manpower, and thus having a greater practical value and a promotion significance.

2. Compared with the Canny edge detection algorithm, the edge detection algorithm based on tensor voting can provide a boundary detection on the basis of an edge detection, and also can provide tensor voting on a grayscale image directly using a two-dimensional circular voting domain, then provide a voting interpretation and provide a boundary extraction based on the saliency of boundary characteristics. This edge detection algorithm based on tensor voting has robustness.

3. The present disclosure can process multi-source remote sensing data, provide more balanced parameter setting, have better universality, and have a greater indicative effect on regional tectonic evolution and plate movement direction.

