CN106778774B - High-resolution remote sensing image artificial ground feature contour detection method - Google Patents
High-resolution remote sensing image artificial ground feature contour detection method Download PDFInfo
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Abstract
The invention relates to a method for detecting the contour of an artificial ground object of a high-resolution remote sensing image. The method comprises the following steps: step 1, firstly, input remote sensing images are inputIDown-sampling to obtainI s Will beI s Convolving with Gaussian kernel function to obtain imageI sc Will beI s MinusI sc Obtaining a difference imageD(ii) a Step 2, comparing the difference images by using a gray statistic comparison windowDPerforming edge detection to obtain a candidate curve set; step 3, verifying and screening the candidate curve set by adopting a standard error function-based verification method to obtain a carefully selected curve set; and 4, smoothing the selected curve set, and outputting a high-resolution remote sensing image artificial ground feature contour detection result. The method and the device furthest excavate the contour information of the artificial ground object in the remote sensing image, and can be applied to accurate extraction of the artificial ground objects such as buildings, roads and the like.
Description
Technical Field
The invention relates to the field of remote sensing image processing, in particular to a method for detecting an artificial ground feature contour of a high-resolution remote sensing image.
Background
The remote sensing image is the comprehensive reflection of the spectrum and the geometric characteristics of the ground target and the phenomenon on the image, not only is a pixel unit set which embodies the brightness characteristic and the chromaticity characteristic, but also has complex spectrum characteristic and structure characteristic. The high-resolution remote sensing image is a very special digital image, and the complexity degree of the high-resolution remote sensing image is far higher than that of a common image. Contour detection of high-resolution remote sensing images is the basis of analysis and understanding of remote sensing information and is a challenge in the field of digital image processing. At present, many scholars continuously propose relevant outline detection theories and methods, but in view of published results, the methods have the following problems:
(1) because the remote sensing image is influenced by a plurality of factors such as a sensor, the position of the sun and the like in the imaging process, the target ground object information represented in the image is not only incomplete, but also contains a large amount of noise. The edge and noise are both shown as large abrupt change of gray scale in the spatial domain, and are reflected as high-frequency components in the frequency domain, and the noise is often detected as an edge point as a result of performing contour detection on the edge and the true contour is not detected due to noise interference.
(2) The remote sensing image has abundant information, and compared with a common image, the remote sensing image contains much more content, the mutual influence and interference of information among different ground objects and an insignificant or fuzzy boundary make it very difficult to extract an interested target contour.
Disclosure of Invention
The invention provides a method for detecting the contour of an artificial ground object of a high-resolution remote sensing image, which takes the high-resolution remote sensing image as a data source and makes a difference image on a scale space, can reduce the interference of noise on the extracted contour and furthest reserve the edge information of the image, and has the advantages of small calculated amount, strong self-adaptive capacity and reliable output result.
The technical scheme adopted for realizing the aim of the invention is as follows: the method comprises the following steps:
step 1: firstly, the input remote sensing image I is sampled down to obtain IsIs shown bysConvolving with a Gaussian kernel function to obtain an image IscIs shown bysSubtract IscObtaining a difference image D;
step 2: carrying out edge detection on the difference image D in the step 1 by using a gray scale statistic comparison window GSC to obtain a candidate curve set Scont；
And step 3: considering the rule that the gray scale statistic comparison window GSC in the step 2 obeys normal distribution, a standard error function-based verification method is adopted to perform verification on the candidate curve set S in the step 2contAll the curves in (1) are verified and screened to obtain a selected curve set Fcont；
And 4, step 4: for the selected curve set F of step 3contAnd smoothing all the curves in the image to output a high-resolution remote sensing image artificial ground feature contour detection result.
Standard deviation of the Gaussian kernel function
Where k is a trade-off coefficient used to balance the jaggies and blur degree of the profile and s is the ratio of adjacent scales in scale space.
The gray scale statistic comparison window GSC is movable, and the moving mode adopts a progressive scanning mode.
The gray scale statistic comparison window GSC is composed of a bright template MLAnd a dark template MDAnd the size of an area contained in the window is determined by the width w of the side surface of the window formed by the adjacent windows, and the value v of the candidate curve in the gray statistic comparison window GSC is calculated according to the following formula:
in the formula (1), M and N are respectively bright templates MLAnd a dark template MDThe number of the pixel points included in (a),
Lmand DnRespectively being bright templates MLM-th pixel in (1) and dark template MDWhen the gray scale statistic comparison window GSC does not contain boundary information, the nth pixel covers a homogeneous area, LnIs constantly equal to DnI.e. delta (L)m,Dn) When the content is equal to 0.5,
when the gray scale statistical comparison window GSC contains significant boundary information, LnIs constantly less than DnI.e. delta (L)m,Dn) When 1, v is M × N; when the value v of the candidate curve is larger than the threshold value T1And taking the candidate curve as a refining curve.
The side width w is a bright template MLAnd a dark template MDThe short side of the minimum external rectangle adopts two values
Wherein the content of the first and second substances,
guarantee bright template MLAnd a dark template MDAt least one row of pixels is covered for detecting a high contrast contour.
Guarantee bright template MLAnd a dark template MDAt least three rows of pixels are covered for detecting the contour containing noise and fuzzy edge.
The standard error function-based verification method is characterized in that a standard error value SER of a curve is calculated by using the following formula:
in the formula (2), the upper limit of integration
In the expression of h
When the standard error value SER of the refined curve is less than the threshold value T2When it is, it is taken as the selection curve.
The smoothing method adopts a Bezier curve smoothing method.
The candidate curve is a result obtained by directly detecting with an edge detection algorithm, wherein the result contains an excessively short or invalid edge, and further verification and screening are required in subsequent steps.
The invention has the beneficial effects that: the method and the device furthest excavate the contour information of the artificial ground object in the remote sensing image, and can be applied to accurate extraction of the artificial ground objects such as buildings, roads and the like.
Drawings
FIG. 1 is an overall process flow diagram of the present invention.
Fig. 2 is a schematic diagram of a gray scale statistical comparison window according to the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings.
FIG. 1 is an overall process flow diagram of the present invention:
in step 101, the input high-resolution remote sensing image I to be processed is a Quickbird full-color image with a spatial resolution of 0.81 m.
In step 102, the high-resolution remote sensing image I is down-sampled to obtain IsWhere the sampling interval is 2 pixels.
In step 103, I is addedsConvolving with a Gaussian kernel function to obtain an image IscIs shown bysSubtract IscObtaining a difference image D in which the standard deviation of the Gaussian kernel function
Where k is a trade-off coefficient used to balance the jaggies and blur degree of the profile and s is the ratio of adjacent scales in scale space. After repeated experiments, k was set to 1.5 and s was set to 0.8.
In step 104, the difference image D in step 103 is subjected to edge detection by using the gray scale statistical comparison window GSC to obtain a candidate curve set ScontThe GSC is movable, the movement mode adopts a progressive scanning mode, and the artificial ground features based on the high-resolution remote sensing image follow the characteristics of regular distributionConsidering the balance between the calculated amount of the subsequent steps and the retention degree of the profile related information, performing tests by using remote sensing images with different sensors and different sizes, finding that the effect of setting the moving step pitch P to be 5 is the best, and setting the gray scale statistic comparison window GSC by using a bright template MLAnd a dark template MDAnd the size of an area contained in the window is determined by the width w of the side surface of the window formed by the adjacent windows, and the value v of the candidate curve in the gray statistic comparison window GSC is calculated according to the following formula:
wherein M and N are respectively bright templates MLAnd a dark template MDThe number of the pixel points included in (a),
Lmand DnRespectively being bright templates MLM-th pixel in (1) and dark template MDWhen the gray scale statistic comparison window GSC does not contain boundary information, the n-th pixel covers a homogeneous region without boundary information, and LnIs constantly equal to DnI.e. delta (L)m,Dn) 0.5) of the total weight of the composition,
when the gray scale statistical comparison window GSC contains significant boundary information, LnIs constantly less than DnI.e. delta (L)m,Dn) 1), v is mxn; when the value v of the candidate curve is larger than the threshold value T1And taking the candidate curve as a refining curve. The width w of the side surface adopts two values
Wherein the content of the first and second substances,
guarantee bright template MLAnd a dark template MDAt least one row of pixels is covered for detecting a high-contrast contour;
guarantee bright template MLAnd a dark template MDAt least three rows of pixels are covered for detecting the contour containing noise and fuzzy edge.
In step 105, considering the rule that the gray scale statistic comparison window GSC in step 104 obeys normal distribution, a standard error function-based verification method is adopted to verify the candidate curve set S in step 4contAll the curves in (1) are verified and screened to obtain a selected curve set FcontThe standard error function-based verification method comprises the following steps of calculating a standard error value SER of a curve by using the following formula:
wherein, the upper limit of integration
In the expression of h
When the standard error value SER of the refined curve is less than the threshold value T2When it is, it is taken as the selection curve.
At step 106, the selected curve set F of step 105 is processedcontAll the curves in (1) are smoothed by a bezier curve smoothing method.
In step 107, the profile is output.
Upon testing, the threshold T in step 1041And a threshold T in step 1062Set to 0.75 and 0.6, respectively.
Fig. 2 is a schematic diagram of a gray scale statistical comparison window according to the present invention.
200 is a part of the difference image D, 201 is a section of contour curve, 202 is a bright template MLAnd 203 is a dark template MDAnd 204 is the side width w.
Claims (7)
1. A method for detecting the contour of an artificial ground object of a high-resolution remote sensing image is characterized by comprising the following steps:
step 1: firstly, the input remote sensing image I is sampled down to obtain IsIs shown bysConvolving with a Gaussian kernel function to obtain an image IscIs shown bysSubtract IscObtaining a difference image D;
step 2: carrying out edge detection on the difference image D in the step 1 by using a gray scale statistic comparison window GSC to obtain a candidate curve set Scont；
And step 3: considering the rule that the gray scale statistic comparison window GSC in the step 2 obeys normal distribution, a standard error function-based verification method is adopted to perform verification on the candidate curve set S in the step 2contAll the curves in (1) are verified and screened to obtain a selected curve set Fcont；
And 4, step 4: for the selected curve set F of step 3contAll the curves in the image are respectively smoothed, and a high-resolution remote sensing image artificial ground feature contour detection result is output;
the gray scale statistic comparison window GSC is composed of a bright template MLAnd a dark template MDAnd the size of an area contained in the window is determined by the width w of the side surface of the window formed by the adjacent windows, and the value v of the candidate curve in the gray statistic comparison window GSC is calculated according to the following formula:
in the formula (1), M and N are respectively bright templates MLAnd a dark template MDThe number of the pixel points included in (a),
Lmand DnRespectively being bright templates MLM-th pixel in (1) and dark template MDThe nth pixel in (1); when the gray scale statistic comparison window GSC does not contain boundary information, covering a homogeneous region, LmIs constantly equal to DnI.e. delta (L)m,Dn) 0.5) of the total weight of the composition,
when the gray scale statistical comparison window GSC contains significant boundary information, LmIs constantly less than DnI.e. delta (L)m,Dn) When 1, v is M × N; when the value v of the candidate curve is larger than the threshold value T1And taking the candidate curve as a refining curve.
2. The method according to claim 1, wherein the gaussian kernel function has a standard deviation of
Where k is a trade-off coefficient used to balance the jaggies and blur degree of the profile and s is the ratio of adjacent scales in scale space.
3. The method for detecting the artificial terrain contour of the high-resolution remote sensing image according to claim 1, wherein the gray scale statistic comparison window GSC is movable, and the moving mode adopts a line-by-line scanning mode.
4. The method for detecting the contour of the artificial ground object of the remote sensing image with high resolution as claimed in claim 1, wherein the width w of the side surface is a bright template MLAnd a dark template MDThe short side of the minimum external rectangle adopts two values
Wherein the content of the first and second substances,
guarantee bright template MLAnd a dark template MDAt least one row of pixels is covered for detecting a high-contrast contour;
guarantee bright template MLAnd a dark template MDCovering at least three rows of pixels for detecting noise-containing, edge-obscuringA contour.
5. The method for detecting the artificial terrain contour of the high-resolution remote sensing image according to claim 1, wherein the standard error function-based verification method is to calculate the standard error value SER of the curve by using the following formula:
in the formula (2), the upper limit of integration
In the expression of h
When the standard error value SER of the refined curve is less than the threshold value T2When it is, it is taken as the selection curve.
6. The method for detecting the artificial terrain contour of the high-resolution remote sensing image according to claim 1, characterized in that the smoothing method adopts a Bezier curve smoothing method.
7. The method for detecting the artificial terrain contour of the high-resolution remote sensing image as claimed in claim 1, wherein the candidate curve is a result obtained by directly detecting with an edge detection algorithm, and contains an excessively short or invalid edge, which requires further verification and screening by subsequent steps.
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