CN108491826A - A kind of extraction method of remote sensing image building - Google Patents

A kind of extraction method of remote sensing image building Download PDF

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Publication number
CN108491826A
CN108491826A CN201810307327.1A CN201810307327A CN108491826A CN 108491826 A CN108491826 A CN 108491826A CN 201810307327 A CN201810307327 A CN 201810307327A CN 108491826 A CN108491826 A CN 108491826A
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building
value
pixel
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CN108491826B (en
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施文灶
程姗
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Fujian Normal University
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Fujian Normal University
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • G06V20/176—Urban or other man-made structures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/24—Aligning, centring, orientation detection or correction of the image
    • G06V10/247—Aligning, centring, orientation detection or correction of the image by affine transforms, e.g. correction due to perspective effects; Quadrilaterals, e.g. trapezoids

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Multimedia (AREA)
  • Theoretical Computer Science (AREA)
  • Image Analysis (AREA)
  • Image Processing (AREA)

Abstract

The present invention relates to a kind of extraction methods of remote sensing image building.Include the following steps：Step 1, multi-spectrum remote sensing image is inputted；Step 2, traversal all pixels point extracts two layerings to the distance between two points randomly selected；Step 3, the cymomotive force of two layerings is calculated；Step 4, differentiate candidate architecture object figure layer and non-building figure layer；Step 5, initialization area quantity；Step 6, candidate architecture object figure layer is handled；Step 7, the iteration stopping condition of determination step 6；Step 8, mark is handled；Step 9, it post-processes；Step 10, result is exported.The building in multi-spectrum remote sensing image can be accurately extracted, especially intensive building can be applied to the update of building in the geographical basis information database of city.

Description

A kind of extraction method of remote sensing image building
Technical field
The present invention relates to a kind of remote sensing image process field, specifically a kind of side of automatically extracting of remote sensing image building Method.
Background technology
Building is one of main geographic element in city, is the important content of various city thematic maps, studies building Extraction is of great significance to integrated survey urban geographic information environment.It is quick with high-resolution remote sensing image acquiring technology Development, the processing of remote sensing image, analysis and application have a better data source, digital product then have more extensively, deeper into Application.Computer image processing technology, pattern-recognition, artificial intelligence etc. all obtain different degrees of progress, for height Effective information in effect ground extraction huge image data provides possibility.But the extraction of building information than other information such as road, The acquisition of water body is much more difficult, and main cause is as follows：
(1) data source is mainly two-dimensional remote sensing image, in most cases lacks direct three-dimensional data；
(2) factors such as different remote sensing image Chang Yinwei spectral regions, resolution ratio, the several picture of sensor and image-forming condition Difference and have larger difference；
(3) its appearance for being showed of different types of building and grain details etc. are ever-changing, show remote sensing images Upper widely different, unified building model library is difficult to set up, this makes automatically extracting for information become extremely difficult；
(4) complexity of scene residing for building, when as relatively low such as contrast, house mutually block, the shade of building itself with And it is in the shade etc. of other atural objects, so thinking that the building for automatically extracting sharpness of border from background is more difficult.
Invention content
The present invention provides a kind of extraction methods of remote sensing image building, can overcome in current remote sensing image and build The difficult problem of object extraction, makes full use of R in remote sensing image, the feature of tri- components of G, B, between feature based vector away from From, the building target in remote sensing image can be detected, be not necessarily to manual intervention, high degree of automation.
Technical solution is used by target to realize the present invention：Method includes the following steps：
Step 1：Input multi-spectrum remote sensing image comprising R, tri- color components of G, B is pre-processed, image I is obtainedin； Step 2：In image IinRGB color SPA in randomly select at 2 points, be denoted as P respectively1And P2, calculate image IinIn picture Vegetarian refreshments PxP is arrived respectively1And P2The distance between, it is denoted as d respectively1xAnd d2x, work as d1x≤d2xWhen, by pixel PxWith P1It merges into same One set, and it is denoted as S1, otherwise by pixel PxWith P2Identity set is merged into, and is denoted as S2, work as S1In there is new pixel to be added after, By P1Location updating be S1The mean place (the equal coordinate value rounding of contraposition horizontalization) of middle all pixels, works as S2In there is new pixel to add After entering, by P2Location updating be S2The mean place (the equal coordinate value rounding of contraposition horizontalization) of middle all pixels；Iterative step 2 The above process, until traversing image IinIn all pixels point, obtain two layering Layer1And Layer2；
Step 3：With following formula to two layering Layer in step 21And Layer2It is handled：
In formula (1), Fltlayer_numTo be layered Layerlayer_numCymomotive force, meanlayer_numTo be layered Layerlayer_num Mean value in RGB color SPA, layer_num are two layering numbers in step 2, and value is 1 and 2, IiIt is i-th Value of a pixel in RGB color SPA, Nlayer_numTo be layered Layerlayer_numIn number of pixels；
Step 4：Work as Fltlayer_numMeet condition T0When, Layer will be layeredlayer_numIt is determined as candidate architecture object figure layer Layercb, otherwise, Layer will be layeredlayer_numIt is determined as non-building figure layer Layernb；
Step 5：By the candidate architecture object figure layer Layer in step 4cbRegion quantity be initialized as C0；
Step 6：With following formula to candidate architecture object figure layer LayercbIt is handled：
In formula (2), ObjF is extracted region object function, HiFor feature of the ith pixel point in RGB color SPA to Amount, RiFor value of the ith pixel point in R component, GiFor value of the ith pixel point in G components, BiFor ith pixel Value of the point in B component, HQFor feature vectors of the barycenter pixel Qc in RGB color SPA of region Q, RQcFor picture Values of the vegetarian refreshments Qc in R component, GQcFor values of the pixel Qc in G components, BQcFor pixel Qc taking in B component Value, pQ(i) belong to the probability of region Q for ith pixel point, | | Hi-HQ| | feature vector H is sought in expressioniAnd HQThe distance between；
Step 7：When | ObjF(k+1)-ObjF(k)When |≤Thr, regional ensemble RS is obtained, enters step 8, otherwise, by C0Value It is updated to C0+ 1, return to step 5, ObjF(k)For the value of ObjF after kth time iteration；
Step 8：Processing is labeled to the regional ensemble RS in step 7；
Step 9：It deletes area and is less than S0Non- construction zone, use rectangular degree and length-width ratio as constraints extract build Object；Step 10：Output extraction building result.
Pretreatment in the step 1 includes geometric correction, radiant correction and contrast enhancing.
Distance in the step 6 | | Hi-HQ| | acquiring method use Chebyshev's distance.
The beneficial effects of the invention are as follows：The building in multi-spectrum remote sensing image can be accurately extracted, it is especially intensive to build Object is built, the update of building in the geographical basis information database of city is can be applied to.
Description of the drawings
Fig. 1 is the overall process flow figure of the present invention.
Specific implementation mode
Detailed description of the present invention specific implementation mode below in conjunction with the accompanying drawings.
In step 101, input includes R, the multi-spectrum remote sensing image of tri- color components of G, B.
In step 102, the input multi-spectrum remote sensing image of step 101 is pre-processed, including geometric correction, radiation school Just enhance with contrast, obtains image Iin。
In step 103, in image IinRGB color SPA in randomly select at 2 points, be denoted as P respectively1And P2。
In step 104, image I is calculatedinIn pixel PxP is arrived respectively1And P2The distance between, it is denoted as d respectively1xWith d2x, work as d1x≤d2xWhen, by pixel PxWith P1Identity set is merged into, and is denoted as S1, otherwise by pixel PxWith P2It merges into same One set, and it is denoted as S2, work as S1In there is new pixel to be added after, by P1Location updating be S1The mean place of middle all pixels is (right Position average coordinates value rounding), work as S2In there is new pixel to be added after, by P2Location updating be S2The average bit of middle all pixels It sets (the equal coordinate value rounding of contraposition horizontalization).
In step 105, judge whether to traverse image IinIn all pixels point, if it is, obtain two layering Layer1And Layer2And 106 are entered step, otherwise enter step 104.
In step 106, with following formula to two layering Layer in step 1051And Layer2It is handled：
In formula (3), Fltlayer_numTo be layered Layerlayer_numCymomotive force, meanlayer_numTo be layered Layerlayer_num Mean value in RGB color SPA, layer_num are two layering numbers in step 2, and value is 1 and 2, IiIt is i-th Value of a pixel in RGB color SPA, Nlayer_numTo be layered Layerlayer_numIn number of pixels.
In step 107, Flt is judgedlayer_numWhether condition T is met0, if it is, Layer will be layeredlayer_numDifferentiate For candidate architecture object figure layer LayercbAnd 109 are entered step, it otherwise will be layered Layerlayer_numIt is determined as non-building figure layer LayernbAnd 108 are entered step, and according to experimental data, when for extracting intensive building, condition T0It need to be set as Fltlayer_num>=465, when for extracting sparse building, condition T0It need to be set as 250≤Fltlayer_num＜ 465.
In step 108, non-building figure layer Layer is obtainednb, indicate that the input multi-spectrum remote sensing image of step 101 is free of Building.
In step 109, by the candidate architecture object figure layer Layer in step 107cbRegion quantity be initialized as C0, it is simultaneous The speed of service and building extraction effect are cared for, by C0It is set as 10.
In step 110, structure realm extracts object function ObjF：
In formula (4), ObjF is extracted region object function, HiFor feature of the ith pixel point in RGB color SPA to Amount, RiFor value of the ith pixel point in R component, GiFor value of the ith pixel point in G components, BiFor ith pixel Value of the point in B component, HQFor feature vectors of the barycenter pixel Qc in RGB color SPA of region Q, RQcFor picture Values of the vegetarian refreshments Qc in R component, GQcFor values of the pixel Qc in G components, BQcFor pixel Qc taking in B component Value, pQ(i) belong to the probability of region Q for ith pixel point, | | Hi-HQ| | feature vector H is sought in expressioniAnd HQBetween cut ratio Avenge husband's distance.
In step 111, with the extracted region object function ObjF in step 110 to candidate architecture object figure layer LayercbIt carries out Processing.
In step 112, ObjF is used(k)The value of ObjF after expression kth time iteration, judges whether ObjF meets stop condition | ObjF(k+1)-ObjF(k)|≤Thr enters step 113 if it is, obtaining regional ensemble RS, otherwise by C0Value be updated to C0It+1 and enters step 111, in order to ensure to extract the accuracy of result, threshold value Thr is set as 10-4。
In step 113, processing is labeled to the regional ensemble RS in step 112.
It in step 114, is post-processed, including deletes area and be less than S0Non- construction zone, with rectangular degree and length and width Than extracting building, the interference of small area atural object in order to prevent, by S as constraints0It is set as 50.
In step 115, result is exported.

Claims (3)

1. a kind of extraction method of remote sensing image building, it is characterised in that include the following steps：
Step 1：Input multi-spectrum remote sensing image comprising R, tri- color components of G, B is pre-processed, image I is obtainedin；
Step 2：In image IinRGB color SPA in randomly select at 2 points, be denoted as P respectively1And P2, calculate image IinIn Pixel PxP is arrived respectively1And P2The distance between, it is denoted as d respectively1xAnd d2x, work as d1x≤d2xWhen, by pixel PxWith P1Merge For identity set, and it is denoted as S1, otherwise by pixel PxWith P2Identity set is merged into, and is denoted as S2, work as S1In there is new pixel to add After entering, by P1Location updating be S1The mean place (the equal coordinate value rounding of contraposition horizontalization) of middle all pixels, works as S2In have new picture After element is added, by P2Location updating be S2The mean place (the equal coordinate value rounding of contraposition horizontalization) of middle all pixels；Iterative step 2 above process, until traversing image IinIn all pixels point, obtain two layering Layer1And Layer2；
Step 3：With following formula to two layering Layer in step 21And Layer2It is handled：
In formula (1), Fltlayer_numTo be layered Layerlayer_numCymomotive force, meanlayer_numTo be layered Layerlayer_num Mean value in RGB color SPA, layer_num are two layering numbers in step 2, and value is 1 and 2, IiIt is i-th Value of a pixel in RGB color SPA, Nlayer_numTo be layered Layerlayer_numIn number of pixels；
Step 4：Work as Fltlayer_numMeet condition T0When, Layer will be layeredlayer_numIt is determined as candidate architecture object figure layer Layercb, otherwise, Layer will be layeredlayer_numIt is determined as non-building figure layer Layernb；
Step 5：By the candidate architecture object figure layer Layer in step 4cbRegion quantity be initialized as C0；
Step 6：With following formula to candidate architecture object figure layer LayercbIt is handled：
In formula (2), ObjF is extracted region object function, HiFor feature of the ith pixel point in RGB color SPA to Amount, RiFor value of the ith pixel point in R component, GiFor value of the ith pixel point in G components, BiFor ith pixel Value of the point in B component, HQFor feature vectors of the barycenter pixel Qc in RGB color SPA of region Q, RQcFor picture Values of the vegetarian refreshments Qc in R component, GQcFor values of the pixel Qc in G components, BQcFor pixel Qc taking in B component Value, pQ(i) belong to the probability of region Q for ith pixel point, | | Hi-HQ| | feature vector H is sought in expressioniAnd HQThe distance between；
Step 7：When | ObjF(k+1)-ObjF(k)When |≤Thr, regional ensemble RS is obtained, enters step 8, otherwise, by C0Value It is updated to C0+ 1, return to step 5, ObjF(k)For the value of ObjF after kth time iteration；
Step 8：Processing is labeled to the regional ensemble RS in step 7；
Step 9：It deletes area and is less than S0Non- construction zone, use rectangular degree and length-width ratio as constraints extract building；
Step 10：Output extraction building result.
2. a kind of extraction method of remote sensing image building according to claim 1, it is characterised in that in step 1 Pretreatment includes geometric correction, radiant correction and contrast enhancing.
3. a kind of extraction method of remote sensing image building according to claim 1, it is characterised in that in step 6 Distance | | Hi-HQ| | acquiring method use Chebyshev's distance.
CN201810307327.1A 2018-04-08 2018-04-08 Automatic extraction method of remote sensing image building Expired - Fee Related CN108491826B (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109635715A (en) * 2018-12-07 2019-04-16 福建师范大学 A kind of remote sensing images building extracting method
CN110298348A (en) * 2019-06-12 2019-10-01 苏州中科天启遥感科技有限公司 Remote sensing image building sample areas extracting method and system, storage medium, equipment

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102015200260A1 (en) * 2014-01-10 2015-07-16 Mitsubishi Electric Corporation Method of creating a descriptor for a scene image
CN104794478A (en) * 2015-05-04 2015-07-22 福建师范大学 Method for extracting buildings with uniform spectral characteristics from remote sensing images
CN105761266A (en) * 2016-02-26 2016-07-13 民政部国家减灾中心 Method of extracting rectangular building from remote sensing image

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102015200260A1 (en) * 2014-01-10 2015-07-16 Mitsubishi Electric Corporation Method of creating a descriptor for a scene image
CN104794478A (en) * 2015-05-04 2015-07-22 福建师范大学 Method for extracting buildings with uniform spectral characteristics from remote sensing images
CN105761266A (en) * 2016-02-26 2016-07-13 民政部国家减灾中心 Method of extracting rectangular building from remote sensing image

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
吴宁: "基于目标识别和参数化技术的城市建筑群三维重建研究", 《中国博士学位论文全文数据库 工程科技Ⅱ辑》 *
施文灶 等: "多光谱遥感影像建筑物提取", 《计算机***应用》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109635715A (en) * 2018-12-07 2019-04-16 福建师范大学 A kind of remote sensing images building extracting method
CN110298348A (en) * 2019-06-12 2019-10-01 苏州中科天启遥感科技有限公司 Remote sensing image building sample areas extracting method and system, storage medium, equipment
CN110298348B (en) * 2019-06-12 2020-04-28 苏州中科天启遥感科技有限公司 Method and system for extracting remote sensing image building sample region, storage medium and equipment

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