CN109993104A - A kind of change detecting method of remote sensing images object hierarchy - Google Patents
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Abstract
The invention discloses a kind of change detecting methods of remote sensing images object hierarchy, specifically include image preliminary treatment, mean filter obtains calculating and the image analysis based on convolutional neural networks；The invention proposes an adaptive k value calculating method, the invention proposes a kind of change detecting methods of remote sensing images object hierarchy, it is whole that preliminary figure is obtained based on the full convolutional neural networks model of depth, and image is finally carried out by binary conversion treatment based on corresponding algorithm, to realize the digitized processing of image, the variation for carrying out the later period to image that the digitized processing based on image can be convenient at this time detects, to improve the precision and accuracy of detection.
Description
Technical field
The present invention relates to field of image processing, more particularly, to a kind of change detecting method of remote sensing images object hierarchy,
It is mainly used for gray processing and binary conversion treatment to traditional remote sensing images.
Background technique
The appearance of remote sensing technology changes the mode that the mankind observe the earth；The acquisition of the remote sensing data of repeatability, so that
The mankind are provided with the new way for periodically understanding wide area ground surface environment dynamic change.With the development of remote sensing technology, optical remote sensing
The spatial resolution of image is increased to meter level from hundred initial meter levels, and even higher, revisiting period has also greatly shortened.With
The development of image technique, to be obtained using the spatial information in remote sensing images as the image analysis method towards geographic object of target
Rapid development is arrived.
At this stage, meter level/sub-meter grade definition satellite remotely-sensed data is numerous, external commercial satellite data such as SPOT,
IKONOS, QuickBird, WorldView, GeoEye etc., China's high-definition remote sensing data have high score No.1, high score No. two numbers
Deng.Such satellite generally uses sun-synchronous orbit to obtain data, and satellite gravity anomaly is flexible, can be by tilting and swinging partially
From substar earth observation.Compared with Landsat satellite of early stage etc. obtains the image-forming mechanism of substar ground mulching type, rice
The spatial information that the geometry of target object is contained in grade remote sensing images is the important information source for identifying earth object.Tradition
Atural object in earth's surface scene is often simply interpreted as that there is similar lambert's system spectral reflectance to emit by Remote Sensing Information Extraction method
Structure, shortage further interpret process to scene space information.However, in high-resolution remote sensing image, the sky of atural object
Between structure externalize the set of multiple adjacent pixels, traditional Remote Sensing Information Extraction means are faced with new challenges.
The remote sensing platforms such as high-resolution Commercial Remote Sensing Satellites abundant, a plurality of types of unmanned vehicles, manned aviation and
Its sensor respectively has advantage, and the acquisition capability of high-definition remote sensing data obtains considerable raising.Remote sensing spatial resolution mentions
Height, to we provide a kind of sense organ cognitions on the spot in person, and current Remote Sensing Image Processing Technology is a lack of mankind's view
Feel the vision processing mechanism in perception.Current remote sensing image processing method is mainly based upon pixel spectral signature, multiple
Our requirements to technology itself are but not achieved in geoscience applications in miscellaneous region.Remote Sensing Information Extraction ability institute visual not as good as ordinary person
The abundant information of acquisition.Therefrom low resolution is increased to sub-meter grade high-resolution, the image feature hair of ground object target to remote sensing image
Significant changes are given birth to, information is more abundant, is difficult to describe numerous complicated on high resolution image comprehensively using single spectral signature
Ground object target.However, the Data Analysis Services ability of complex environment remote sensing is not correspondingly improved but, it is ever-increasing
Most of data in mass remote sensing data do not obtain effective analysis and utilization.It can be seen that realizing remotely-sensed data to sky
Between information intelligent convert image Cognitive Mode be in urgent need with correlative studys such as image understandings.How high-resolution is made full use of
The priori knowledges such as the object spectrum, space structure, the image-forming information that are contained in remote sensing information establish computable remote sensing information solution
Analysis mechanism analyzes complex scene environment, is the difficult point that high-resolution remote sensing image understands.
The raising of spatial resolution, so that the atural object that we recognize is rendered as pixel set in remote sensing image.In high score
In the remote sensing image of resolution, need to go to obtain by way of image procossing the pixel set and its space that it is reflected on image
Structure.In last decade, to obtain and using pixel set in image as the various partitioning algorithms of target and object-oriented
Image analysis method (OBIA/ GEOBIA) rapidly develops, and has been a much progress of technology development for Pixel-level.With
This improves Remote Sensing Information Extraction ability using atural object spatial information and is more paid attention to simultaneously, is existed using the key of spatial information
In obtain image object, image object refer on remote sensing image with the consistent pixel set of atural object intrinsic time theory.Therefore, synthesis is distant
The all information such as spectrum and spatial information that sense image can reflect develop the remote sensing image information processing skill of image object hierarchy
Art has been fundamentally to improve the certainty of Remote Sensing Information Extraction ability, and further realize the base of remote sensing image interpretation reasoning
Plinth.
Summary of the invention
The present invention is to overcome above situation insufficient, it is desirable to provide a kind of technical solution that can solve the above problem.
A kind of change detecting method of remote sensing images object hierarchy specifically includes image preliminary treatment, mean filter obtains
Calculating and image analysis；
The initial pictures input full convolutional neural networks model of depth trained in advance is obtained the depth by S1, image preliminary treatment
Spend the probability that each pixel in the initial pictures of full convolutional neural networks model output is character pixels point, wherein institute
Stating the full convolutional neural networks model of depth is to advance with the training image of the real estate for being labeled with character to be trained to obtain；
Then the pixel in the initial pictures is classified, wherein the pixel that probability is greater than predetermined probabilities threshold value is classified as character
Pixel；Multiple trained figure layers are obtained by multiple image procossing, and training map overlay is obtained into preliminary figure；
S2, the preliminary figure for exporting step S1 carry out mean filter and obtain calculating: using Sauvola algorithm to gray level image
Threshold segmentation is carried out, as the formula:
In formula (1), D (x, y) represents the contrast value of pixel, is calculated based on formula (1)；
Wherein T indicates the mean filter acquisition of current pixel, and m and s respectively indicate the gray scale of pixel within the scope of current neighborhood
Mean value and standard deviation, k are an adjustment factors, and the response for control algolithm to picture contrast, R is to gray standard deviation
An adjusting it is related with the gray scale order of image；
E (x, y) represents whether pixel is marginal point, and if it is marginal point, then the value is 1, and otherwise the value is 0, Ne representative
The number of marginal point in current pixel point contiguous range, the k value being calculated by formula (1), actually current picture
Why the contrast mean value at edge in vegetarian refreshments neighborhood does not use the contrast information of contiguous range all the points, is because of non-side
The variation of edge point local gray-value is smaller, and contrast cannot reflect the contrast distribution feature of neighborhood, bring formula (1) into formula (2)
It obtains new mean filter and obtains calculation formula:
In formula (3), when calculating threshold value other than using neighborhood of pixel points grayscale information, the comparison of contiguous range is also introduced
Information is spent, can adapt to the inconsistent situation of picture contrast；
S3, image analysis: image analysis method is as the formula (4):
G (x, y) represents the gray value of current pixel point, and T is that the mean filter being calculated using formula (3) is obtained, and Ne is represented
The number of marginal point in current pixel point contiguous range, only when the gray value of the pixel less than its mean filter obtain, and
When the number of the neighborhood of pixel points range inward flange pixel is greater than neighborhood diameter (current point is in adjacent edges), the pixel is determined
Point is foreground pixel, and otherwise the pixel is background pixel, traverses all the points on image, calculates mean filter and obtains and count
The number of marginal point in contiguous range completes the binaryzation of image using formula (4).
Beneficial effects of the present invention: the invention proposes an adaptive k value calculating method, the invention proposes one
The change detecting method of kind remote sensing images object hierarchy, it is whole that preliminary figure is obtained based on the full convolutional neural networks model of depth
Shape, and image is finally carried out by binary conversion treatment based on corresponding algorithm, to realize the digitized processing of image, it is based at this time
The variation for carrying out the later period to image that the digitized processing of image can be convenient detects, to improve the precision of detection and accurate
Property.
Additional aspect and advantage of the invention will be set forth in part in the description, and will partially become from the following description
Obviously, or practice through the invention is recognized.
Detailed description of the invention
Fig. 1 is inventive algorithm process.
Specific embodiment
In the embodiment of the present invention, a kind of change detecting method of remote sensing images object hierarchy specifically includes image and tentatively locates
Reason, mean filter obtain calculating and image analysis；
The initial pictures input full convolutional neural networks model of depth trained in advance is obtained the depth by S1, image preliminary treatment
Spend the probability that each pixel in the initial pictures of full convolutional neural networks model output is character pixels point, wherein institute
Stating the full convolutional neural networks model of depth is to advance with the training image of the real estate for being labeled with character to be trained to obtain；
Then the pixel in the initial pictures is classified, wherein the pixel that probability is greater than predetermined probabilities threshold value is classified as character
Pixel；Multiple trained figure layers are obtained by multiple image procossing, and training map overlay is obtained into preliminary figure；
S2, the preliminary figure for exporting step S1 carry out mean filter and obtain calculating: using Sauvola algorithm to gray level image
Threshold segmentation is carried out, as the formula:
In formula (1), D (x, y) represents the contrast value of pixel, is calculated based on formula (1)；
Wherein T indicates the mean filter acquisition of current pixel, and m and s respectively indicate the gray scale of pixel within the scope of current neighborhood
Mean value and standard deviation, k are an adjustment factors, and the response for control algolithm to picture contrast, R is to gray standard deviation
An adjusting it is related with the gray scale order of image；
E (x, y) represents whether pixel is marginal point, and if it is marginal point, then the value is 1, and otherwise the value is 0, Ne representative
The number of marginal point in current pixel point contiguous range, the k value being calculated by formula (1), actually current picture
Why the contrast mean value at edge in vegetarian refreshments neighborhood does not use the contrast information of contiguous range all the points, is because of non-side
The variation of edge point local gray-value is smaller, and contrast cannot reflect the contrast distribution feature of neighborhood, bring formula (1) into formula (2)
It obtains new mean filter and obtains calculation formula:
In formula (3), when calculating threshold value other than using neighborhood of pixel points grayscale information, the comparison of contiguous range is also introduced
Information is spent, can adapt to the inconsistent situation of picture contrast；
S3, image analysis: image analysis method is as the formula (4):
G (x, y) represents the gray value of current pixel point, and T is that the mean filter being calculated using formula (3) is obtained, and Ne is represented
The number of marginal point in current pixel point contiguous range, only when the gray value of the pixel less than its mean filter obtain, and
When the number of the neighborhood of pixel points range inward flange pixel is greater than neighborhood diameter (current point is in adjacent edges), the pixel is determined
Point is foreground pixel, and otherwise the pixel is background pixel, traverses all the points on image, calculates mean filter and obtains and count
The number of marginal point in contiguous range completes the binaryzation of image using formula (4).
It is obvious to a person skilled in the art that invention is not limited to the details of the above exemplary embodiments, Er Qie
In the case where without departing substantially from spirit or essential attributes of the invention, the present invention can be realized in other specific forms.Therefore, no matter
From the point of view of which point, the present embodiments are to be considered as illustrative and not restrictive, and the scope of the present invention is by appended power
Benefit requires rather than above description limits, it is intended that all by what is fallen within the meaning and scope of the equivalent elements of the claims
Variation is included within the present invention.
Claims (3)
1. a kind of change detecting method of remote sensing images object hierarchy, which is characterized in that specifically include image preliminary treatment, mean value
Filtering obtains calculating and image analysis；
The initial pictures input full convolutional neural networks model of depth trained in advance is obtained the depth by S1, image preliminary treatment
Spend the probability that each pixel in the initial pictures of full convolutional neural networks model output is character pixels point, wherein institute
Stating the full convolutional neural networks model of depth is to advance with the training image of the real estate for being labeled with character to be trained to obtain；
Then the pixel in the initial pictures is classified, wherein the pixel that probability is greater than predetermined probabilities threshold value is classified as character
Pixel；Multiple trained figure layers are obtained by multiple image procossing, and training map overlay is obtained into preliminary figure；
S2, the preliminary figure for exporting step S1 carry out mean filter and obtain calculating: using Sauvola algorithm to gray level image
Threshold segmentation is carried out, as the formula:
In formula (1), D (x, y) represents the contrast value of pixel, is calculated based on formula (1)；
Wherein T indicates the mean filter acquisition of current pixel, and m and s respectively indicate the gray scale of pixel within the scope of current neighborhood
Mean value and standard deviation, Ne represent the number of the marginal point in current pixel point contiguous range, the k being calculated by formula (1)
Value brings formula (1) into formula (2) and obtains new mean filter acquisition calculation formula:
In formula (3), when calculating threshold value other than using neighborhood of pixel points grayscale information, the comparison of contiguous range is also introduced
Information is spent, can adapt to the inconsistent situation of picture contrast；
S3, image analysis: image analysis method is as the formula (4):
；
G (x, y) represents the gray value of current pixel point, and T is that the mean filter being calculated using formula (3) is obtained, and determines the picture
Vegetarian refreshments is foreground pixel, and otherwise the pixel is background pixel, traverses all the points on image, calculates mean filter and obtains and unite
The number for counting marginal point in contiguous range completes the binaryzation of image using formula (4).
2. the change detecting method of remote sensing images object hierarchy according to claim 1, which is characterized in that k is a tune
Coefficient, the response for control algolithm to picture contrast are saved, R is the gray scale of an adjusting and image to gray standard deviation
Order is related；E (x, y) represents whether pixel is marginal point, and if it is marginal point, then the value is 1, and otherwise the value is 0.
3. the change detecting method of remote sensing images object hierarchy according to claim 1, Ne represent current pixel vertex neighborhood
The number of marginal point in range, only when the gray value of the pixel is obtained less than its mean filter, and the neighborhood of pixel points model
When enclosing the number of inward flange pixel greater than neighborhood diameter (i.e. current point is in adjacent edges).
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