WO2021248599A1 - Procédé et système d'identification automatique de point dont la catégorie est anormale - Google Patents

Procédé et système d'identification automatique de point dont la catégorie est anormale Download PDF

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WO2021248599A1
WO2021248599A1 PCT/CN2020/100451 CN2020100451W WO2021248599A1 WO 2021248599 A1 WO2021248599 A1 WO 2021248599A1 CN 2020100451 W CN2020100451 W CN 2020100451W WO 2021248599 A1 WO2021248599 A1 WO 2021248599A1
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index
image
pattern
abnormal
images
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PCT/CN2020/100451
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English (en)
Chinese (zh)
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李冲
李昊霖
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自然资源部四川测绘产品质量监督检验站(四川省测绘产品质量监督检验站)
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Publication of WO2021248599A1 publication Critical patent/WO2021248599A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/90—Determination of colour characteristics
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10032—Satellite or aerial image; Remote sensing
    • G06T2207/10036—Multispectral image; Hyperspectral image

Definitions

  • the present invention relates to the technical field of image processing, and in particular to a method and system for automatically identifying types of abnormal patterns.
  • the purpose of the present invention is to provide a method and system for automatically identifying types of abnormal patterns.
  • the present invention provides the following solutions:
  • a method for automatically identifying types of abnormal patterns including:
  • each patch image According to the size of the comprehensive spectral index of each patch image, sort each patch image by element type;
  • If there are abnormal categorical patterns add a set number of pattern images before and/or after the pattern image sequence, and perform category anomaly recognition on the added pattern images. If there are categorical abnormalities in the added pattern image For spots, skip to the step of "adding a set number of spot images before and/or after the spot image sequence for identification" until there are no abnormal spots in the added spot images;
  • the method before the cropping of the multispectral remote sensing image based on the geometric range of the pattern, the method further includes:
  • the sorting of each map spot image according to the size of the comprehensive spectral index of each map spot image by element category specifically includes:
  • each patch image is sorted by feature class.
  • the method for detecting category abnormal patterns includes:
  • the abnormal pattern is all the patterns between the adjacent pattern image and the first end of the pattern sequence, and the first end is the distance of the pattern sequence. The closer end of the adjacent spot image.
  • the method before calculating the comprehensive spectral index of each spot image, the method further includes:
  • Stretching processing is performed on the differential building index and the normalized vegetation index and water body index.
  • the method further includes:
  • the abnormal pattern image of the category recognized by the computer is output to the human-computer interaction terminal for manual secondary recognition.
  • the cropping process of the multi-spectral remote sensing image is performed in a computer memory.
  • the present invention also provides an automatic identification system for abnormal patterns, including:
  • the to-be-identified multi-spectral remote sensing image acquisition module is used to acquire the to-be-identified multi-spectral remote sensing image
  • the patch image cropping module is used for cropping the multi-spectral remote sensing image based on the geometric range of the patch to obtain the image of the patch that is prone to error or confusion;
  • the comprehensive spectral index calculation module is used to calculate the comprehensive spectral index of each patch image, and the comprehensive spectral index is composed of a weighted combination of vegetation index, water index, and differential building index to distinguish the types of the patch elements;
  • the sorting module is used to sort the image of each patch according to the size of the comprehensive spectral index of the image of each patch;
  • the category abnormality determination module is used to determine whether there are category abnormal spots in the set number of spot images before and after the spot image sequence, and to determine whether there are category abnormal spots in the added spot images.
  • system further includes:
  • the initial parameter determination module is used to determine the error-prone or confusing element types and the weight coefficients of the vegetation index, the water body index, and the differential building index in the comprehensive spectral index used to distinguish the error-prone or confusing element types based on historical data.
  • the sorting module specifically includes:
  • a numerical value determining unit for determining the median or average value of the comprehensive spectral index of each of the pattern images
  • the sorting unit is used for sorting each spot image according to the size of the middle value or average value of the comprehensive spectral index of the spot image.
  • the present invention discloses the following technical effects: the method and system for automatically identifying abnormal patterns provided by the present invention first extract the error-prone or confusing patterns in the multi-spectral remote sensing image to be identified, and then correct Calculate the spectral index of each pattern that can distinguish error-prone or confusing patterns, and sort the available patterns according to the size of the spectral index, and finally classify abnormalities by setting the number of patterns at both ends of the sequence Detection to determine the category of abnormal patterns.
  • the invention realizes the automatic recognition of the abnormal pattern category and improves the detection efficiency.
  • FIG. 1 is a schematic flowchart of a method for automatically identifying abnormal patterns according to Embodiment 1 of the present invention
  • Fig. 2 is a schematic structural diagram of an automatic identification system for type abnormal patterns provided by Embodiment 2 of the present invention.
  • Fig. 1 is a schematic flow chart of the method for automatic identification of abnormal patterns according to Embodiment 1 of the present invention.
  • the method for automatic identification of abnormal patterns provided by this embodiment includes the following steps:
  • Step 101 Obtain a multi-spectral remote sensing image to be identified
  • Step 102 Based on the geometric range of the pattern, crop the multispectral remote sensing image to obtain an image of pattern prone to error or confusion;
  • Step 103 Calculate the comprehensive spectral index of each map spot image, where the comprehensive spectral index is composed of a weighted combination of vegetation index, water index, and differential building index to distinguish the types of map spot elements;
  • Step 104 According to the size of the comprehensive spectral index of each map spot image, sort the map spot images according to the feature class;
  • Step 105 Determine whether there are abnormal patterns in the set number of pattern images before and after the pattern image sequence
  • Step 106 If there are abnormal categorical patterns, add a set number of pattern images before and/or after the pattern image sequence, and perform category abnormality recognition on the added pattern images. If the added pattern images are If there are abnormal patterns in the category, skip to the step of "adding a set number of pattern images before and/or after the pattern image sequence for identification" until there are no abnormal patterns in the added pattern image;
  • Step 107 Output the abnormal pattern image.
  • the geometric range of each map spot is sequentially obtained from the ground cover vector data, and the image cropping function is defined.
  • the cropped range is the geometric range of the map spot
  • the cropped image is the multi-spectral remote sensing image to be identified
  • the number of bands of the multi-spectral remote sensing image to be identified should be no less than 4, including at least red, green, blue, near-infrared and other bands.
  • the image cropping function defines the image data processing method for the speckle spectral index. Among them, the preferred Yes, the processing is carried out in the computer memory, and the cutting results are not output to the computer hard disk. Calculate the comprehensive spectral index of each patch image.
  • the comprehensive spectral index is composed of a weighted combination of vegetation index, water index, and differential building index to distinguish the types of patch elements.
  • other spectral feature data that can distinguish the types of pattern elements can also be used.
  • the middle value of the comprehensive spectral index of the pattern image can be selected to represent that the patterns are sorted according to their size.
  • the average value of the integrated spectral index of the pattern image can also be selected to represent the pattern for pattern sorting.
  • the patches belonging to the same feature class are sorted separately, for example, all feature category attributes are paddy fields.
  • the patches of the class are sorted as a group, and the patches of all the feature category attributes are lakes are sorted as a group.
  • the method for determining the intermediate value of the comprehensive spectral index of the pattern image is as follows: when the number of pixels in the pattern is an odd number, the middle value in the pixel comprehensive spectral index sequence is the middle of the comprehensive spectral index of the pattern. When the number of pixels in the pattern is an even number, the two middle values in the pixel comprehensive spectral index sequence are taken out, and the average of the two values is calculated, which is the middle value of the pattern comprehensive spectral index of the pattern.
  • the category abnormal pattern after determining the abnormal pattern, can be directly output, or the category abnormal pattern can be output to the human-computer interaction terminal, and the secondary identification is manually performed.
  • step 102 may further include: determining the element types that are prone to error or confusion based on historical data and the vegetation index, water index, and the comprehensive spectral index used to distinguish the element types that are prone to error or confusion.
  • the weight coefficient of the difference building index may be determining the element types that are prone to error or confusion based on historical data and the vegetation index, water index, and the comprehensive spectral index used to distinguish the element types that are prone to error or confusion.
  • the spectral information of the non-visible light band and the visible light band can obtain the weight coefficients of the vegetation index, the water index and the difference building index in the comprehensive spectral index of the pattern that can distinguish the error-prone and confusing element types.
  • the method for detecting category abnormal patterns may specifically be:
  • the abnormal pattern is all the patterns between the adjacent pattern image and the first end of the pattern sequence, and the first end is the distance from the phase in the pattern sequence. The nearest end of the adjacent spot image.
  • the specific operation method is: to judge the comprehensive spectral index of two adjacent patterns among the 5 patterns (such as the middle value of the comprehensive spectral index) Whether the difference between is greater than the set threshold, the set threshold can generally be set to 0.04, if the difference between the integrated spectral index of the 4th and 5th spots (such as the middle value of the integrated spectral index) is greater than the set threshold, then It is considered that the first 4 spots are all abnormal spots.
  • each index needs to be preprocessed.
  • the preprocessing process may include normalization processing and stretching processing. The specific process is as follows:
  • B nir is the near-infrared band of remote sensing images
  • B red is the red band of remote sensing images.
  • B nir is the near-infrared band of remote sensing images
  • B green is the green band of remote sensing images.
  • k is the calculation coefficient, which can generally be set to 0.5
  • B blue is the blue band of the remote sensing image
  • B red is the red band of the remote sensing image
  • B green is the green band of the remote sensing image.
  • the normalized vegetation index, normalized water index, and differential building index are stretched to stretch the value range to [0,1], and the normalized vegetation index and normalized water index are processed according to the following formula Stretching treatment.
  • Pixel v is the pixel value on the original normalized water body or vegetation index
  • Pixel' v is the pixel value after stretching.
  • the differential building index is stretched according to the following formula.
  • Pixel v is the pixel value on the original differential building index
  • Pixel' v is the pixel value after stretching
  • Pixel v_min is the minimum pixel value of the original differential building index for the entire scene
  • Pixel v_max is the original differential building for the entire scene The maximum pixel value of the object index.
  • the vegetation index NDVI, the water index NDWI, and the building index NSBI are comprehensively weighted according to the following formula to calculate the comprehensive spectral index NCI of the pattern.
  • k 1 is the coefficient of NDVI
  • k 2 is the coefficient of NDWI
  • k 3 is the coefficient of NSBI.
  • the values of k 1 , k 2 , and k 3 can be determined based on the statistical analysis results of the spectral information of each type of element.
  • the NCI of this type of element is calculated When the value of k 1 is set to 1, the value of k 2 and k 3 is set to 0; when the value of NDWI is easier to distinguish a certain feature category from other feature categories, when calculating the NCI of this type of feature, k The value of 2 is set to 1, the value of k 1 and k 3 is set to 0; when the value of NSBI is easier to distinguish a certain feature category from other feature categories, when calculating the NCI of this type of feature, the value of k 3 is set The values of 1, k 1 and k 2 are set to zero.
  • FIG. 2 is a schematic structural diagram of the automatic identification system for abnormal patterns according to Embodiment 2 of the present invention.
  • the automatic identification system for abnormal patterns provided by this embodiment includes:
  • the to-be-identified multi-spectral remote sensing image acquisition module 201 is used to acquire the to-be-identified multi-spectral remote sensing image
  • the patch image cropping module 202 is configured to crop the multi-spectral remote sensing image based on the geometric range of the patch to obtain an image with a pattern that is prone to error or confusion;
  • the comprehensive spectral index calculation module 203 is used to calculate the comprehensive spectral index of each pattern image, the comprehensive spectral index is composed of a weighted combination of vegetation index, water index, and differential building index to distinguish the types of pattern elements;
  • the sorting module 204 is used for sorting each spot image according to the size of the comprehensive spectral index of each spot image according to the feature class;
  • the category abnormality determining module 205 is used to determine whether there are abnormal categorical patterns in the set number of pattern images before and after the pattern image sequence, and to determine whether there are abnormal categorical patterns in the added pattern image.
  • system further includes:
  • the initial parameter determination module is used to determine the error-prone or confusing element types based on historical data and the weight coefficients of the vegetation index, water index, and differential building index in the comprehensive spectral index used to distinguish error-prone or confusing element types.
  • the sorting module 204 specifically includes:
  • a numerical value determining unit for determining the median or average value of the comprehensive spectral index of each of the pattern images
  • the sorting unit is used for sorting each spot image according to the size of the middle value or average value of the comprehensive spectral index of the spot image.
  • the category abnormality determining module 205 specifically includes:
  • the judging unit is used for judging whether the difference of the comprehensive spectral index of adjacent spot images is greater than a set threshold
  • the category abnormal pattern determination unit is used to determine the category of all the patterns between the adjacent pattern image and the first end of the pattern sequence when the difference of the comprehensive spectral index of the adjacent pattern image is greater than the set threshold value
  • the first end is an end of the pattern sequence that is closer to the image of the adjacent pattern.
  • system further includes:
  • the normalization processing module is used to perform normalization processing on the vegetation index and the water body index;
  • the stretching processing module is used to perform stretching processing on the differential building index and the normalized vegetation index and water body index.
  • the method and system for automatic identification of type abnormal patterns realizes automatic extraction of large pattern and same-spectrum foreign matter errors, improves the efficiency and reliability of surface coverage data quality detection, and effectively improves the quality of results of geographic and national conditions.

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Abstract

Procédé et système d'identification automatique d'un point dont la catégorie est anormale. Ledit procédé consiste : à acquérir des images de détection à distance multispectrale à identifier (101) ; à recadrer, en fonction de la plage géométrique de points, l'image de détection à distance multispectrale pour obtenir des images des points de catégories sujettes à erreur ou facilement confondues (102) ; à calculer l'indice spectral complet des images des points (103) ; à trier, selon la taille de l'indice spectral complet des images des points, les images des points par classes d'éléments (104) ; à déterminer s'il existe un point dont la catégorie est anormale parmi le nombre défini d'images des points précédant et suivant une séquence d'images de points (105) ; si tel est le cas, à ajouter un nombre défini d'images de points précédant et/ou suivant la séquence d'images de points et à effectuer une identification d'anomalies de catégories sur les images ajoutées de points, s'il existe un point de catégorie anormale parmi les images ajoutées de points, à passer à l'étape « ajout d'un nombre défini d'images de points précédant et/ou suivant la séquence d'images de points pour identification » jusqu'à disparition des points dont la catégorie est anormale parmi les images ajoutées de points (106). Ledit procédé peut implémenter une identification automatique d'un point dont la catégorie est anormale.
PCT/CN2020/100451 2020-06-12 2020-07-06 Procédé et système d'identification automatique de point dont la catégorie est anormale WO2021248599A1 (fr)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113484245A (zh) * 2021-07-05 2021-10-08 重庆市规划和自然资源调查监测院 一种丘陵山区水田种植模式遥感快速监测方法、系统及计算机可读存储介质
CN114491187A (zh) * 2022-01-20 2022-05-13 重庆市规划和自然资源调查监测院 一种自然资源智能监测集成系统
CN118155005A (zh) * 2024-05-13 2024-06-07 成都坤舆空间科技有限公司 一种基于RAFT-Stereo算法的生态修复图斑匹配分类方法

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120271609A1 (en) * 2011-04-20 2012-10-25 Westerngeco L.L.C. Methods and computing systems for hydrocarbon exploration
KR101756380B1 (ko) * 2017-03-06 2017-07-10 한국지질자원연구원 공작석 초분광 영상을 이용한 반암동 광체 탐지 방법
CN109726679A (zh) * 2018-12-28 2019-05-07 常州市星图测绘科技有限公司 一种遥感分类误差空间分布制图方法
CN110135310A (zh) * 2019-04-30 2019-08-16 云南财经大学 一种基于单变量特征选择方法的农作物遥感识别方法
CN111144249A (zh) * 2019-12-16 2020-05-12 广州地理研究所 基于自动优化mesma的地表覆盖类型确定方法
CN111178169A (zh) * 2019-12-12 2020-05-19 广州地理研究所 一种基于遥感影像的城市地表覆盖物精细分类方法及装置

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103593853B (zh) * 2013-11-29 2016-05-11 武汉大学 基于联合稀疏表达的遥感影像多尺度面向对象分类方法
CN103593852A (zh) * 2013-11-29 2014-02-19 中国科学院光电研究院 基于同质图斑的高光谱影像异常探测方法
CN104408463B (zh) * 2014-10-15 2020-05-12 中国土地勘测规划院 一种高分辨率建设用地图斑识别方法
EP3173975A1 (fr) * 2015-11-30 2017-05-31 Delphi Technologies, Inc. Procédé d'identification de points candidats comme points caractéristiques possibles d'un motif d'étalonnage à l'intérieur d'une image du motif d'étalonnage
CN110363798B (zh) * 2019-07-24 2022-02-18 宁波市测绘和遥感技术研究院 一种遥感影像解译样本集的生成方法

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20120271609A1 (en) * 2011-04-20 2012-10-25 Westerngeco L.L.C. Methods and computing systems for hydrocarbon exploration
KR101756380B1 (ko) * 2017-03-06 2017-07-10 한국지질자원연구원 공작석 초분광 영상을 이용한 반암동 광체 탐지 방법
CN109726679A (zh) * 2018-12-28 2019-05-07 常州市星图测绘科技有限公司 一种遥感分类误差空间分布制图方法
CN110135310A (zh) * 2019-04-30 2019-08-16 云南财经大学 一种基于单变量特征选择方法的农作物遥感识别方法
CN111178169A (zh) * 2019-12-12 2020-05-19 广州地理研究所 一种基于遥感影像的城市地表覆盖物精细分类方法及装置
CN111144249A (zh) * 2019-12-16 2020-05-12 广州地理研究所 基于自动优化mesma的地表覆盖类型确定方法

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
LIU, WEI: "Auto-identify Classification Technology for LUCC Information Based on Remote Sensing Data", DOCTORAL DISSERTATION, 15 November 2012 (2012-11-15), CN, pages 1 - 158, XP009532889 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113484245A (zh) * 2021-07-05 2021-10-08 重庆市规划和自然资源调查监测院 一种丘陵山区水田种植模式遥感快速监测方法、系统及计算机可读存储介质
CN113484245B (zh) * 2021-07-05 2022-11-22 重庆市规划和自然资源调查监测院 一种丘陵山区水田种植模式遥感快速监测方法、系统及计算机可读存储介质
CN114491187A (zh) * 2022-01-20 2022-05-13 重庆市规划和自然资源调查监测院 一种自然资源智能监测集成系统
CN118155005A (zh) * 2024-05-13 2024-06-07 成都坤舆空间科技有限公司 一种基于RAFT-Stereo算法的生态修复图斑匹配分类方法

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