CN107203790A - Utilize the Chinese land noctilucence Classification in Remote Sensing Image Accuracy Assessment of two stage sampling model - Google Patents

Utilize the Chinese land noctilucence Classification in Remote Sensing Image Accuracy Assessment of two stage sampling model Download PDF

Info

Publication number
CN107203790A
CN107203790A CN201710488761.XA CN201710488761A CN107203790A CN 107203790 A CN107203790 A CN 107203790A CN 201710488761 A CN201710488761 A CN 201710488761A CN 107203790 A CN107203790 A CN 107203790A
Authority
CN
China
Prior art keywords
remote sensing
luminous
accuracy
classification
sampling
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN201710488761.XA
Other languages
Chinese (zh)
Inventor
黄冬梅
王振华
徐首珏
苏诚
孙婧琦
梁素玲
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shanghai Maritime University
Original Assignee
Shanghai Maritime University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shanghai Maritime University filed Critical Shanghai Maritime University
Priority to CN201710488761.XA priority Critical patent/CN107203790A/en
Publication of CN107203790A publication Critical patent/CN107203790A/en
Pending legal-status Critical Current

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00Pattern recognition
    • G06F18/20Analysing
    • G06F18/21Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/217Validation; Performance evaluation; Active pattern learning techniques
    • G06F18/2193Validation; Performance evaluation; Active pattern learning techniques based on specific statistical tests
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06FELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00Pattern recognition
    • G06F18/20Analysing
    • G06F18/24Classification techniques
    • G06F18/241Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06VIMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00Scenes; Scene-specific elements
    • G06V20/10Terrestrial scenes
    • G06V20/176Urban or other man-made structures

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Biology (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Probability & Statistics with Applications (AREA)
  • Multimedia (AREA)
  • Image Analysis (AREA)

Abstract

本发明涉及一种利用二级抽样模型的中国陆地夜光遥感分类精度评价方法,其该方法具体包括以下步骤:步骤S1,在NPP/VIIRS系列数据实现对中国陆地区域的建成区提取,得到未验证的提取结果;步骤S2,建立二级抽样模型,完成对评价区域的抽样,得到可信的检验数据集;步骤S3,使用误差矩阵验证方法,对夜光数据的初步提取进行分区验证,并最终得到全体夜光数据分类结果的精度评价。利用二级抽样模型的夜光遥感分类精度评价方法,完成对夜光遥感数据的系统、有效地实现对大面积夜光遥感分类结果的精度评价,对夜光遥感城镇提取结果质量检验,能够解决了以往验证集过少,评价参数不全面的问题,达到了一种全面精确的验证结果。

The present invention relates to a method for evaluating the accuracy of China's land luminous remote sensing classification using a two-level sampling model. The method specifically includes the following steps: Step S1, extracting the built-up areas of China's land areas from the NPP/VIIRS series data, and obtaining unverified The extraction results of the luminous data; step S2, establish a secondary sampling model, complete the sampling of the evaluation area, and obtain a credible inspection data set; step S3, use the error matrix verification method to perform partition verification on the preliminary extraction of luminous data, and finally obtain Accuracy evaluation of the classification results of the whole luminous data. Using the classification accuracy evaluation method of luminous remote sensing using the two-stage sampling model, the system of luminous remote sensing data is completed, and the accuracy evaluation of large-area luminous remote sensing classification results is realized effectively. The quality inspection of the extraction results of luminous remote sensing towns can solve the previous verification set Too few, the evaluation parameters are not comprehensive, and a comprehensive and accurate verification result has been achieved.

Description

利用二级抽样模型的中国陆地夜光遥感分类精度评价方法Evaluation method of China land night light remote sensing classification accuracy using two-level sampling model

技术领域technical field

本发明涉及遥感图像质量检验技术领域,具体地说,是一种利用二级抽样模型的中国陆地夜光遥感分类精度评价方法。The invention relates to the technical field of remote sensing image quality inspection, in particular to a method for evaluating classification accuracy of China's land luminous remote sensing classification using a two-level sampling model.

背景技术Background technique

夜光遥感数据能探测到城市灯光甚至小规模居民地等发出的低强度灯光,并将其与黑暗的乡村背景区分开来,间接的反映了城镇的分布,为大尺度的城市检测研究提供了新思路。但由于夜光的溢出效应等因素,根据夜光提取城镇时可能导致城镇面积提取结果大于实际值等问题,因此在应用前必须对提取结果进行质量验证。Night light remote sensing data can detect low-intensity lights from urban lights and even small-scale residential areas, and distinguish them from dark rural backgrounds, indirectly reflecting the distribution of cities and towns, and providing new insights into large-scale urban detection research. train of thought. However, due to factors such as the spillover effect of night light, extracting cities and towns based on night light may lead to problems such as the urban area extraction result being larger than the actual value. Therefore, the quality of the extraction results must be verified before application.

传统的验证方法如在长三角城市化格局的研究中仅将城市提取结果的面积属性和国家统计数据进行了对比验证;在中国城市扩张研究中以Landsat数据作为验证集选取15个大中城市作为训练集,利用误差矩阵对提取结果进行了系统地验证;利用景观形状指数、聚集度指数、边缘面积比和连接度指数4个定量指标验证夜光遥感数据提取结果的形状面积相似程度;采用随机抽样的方式抽取1014个样本点对浙江省的夜光数据提取结果进行了验证。Traditional verification methods, for example, in the study of the urbanization pattern of the Yangtze River Delta, only compared and verified the area attributes of the city extraction results with national statistical data; in the study of urban expansion in China, 15 large and medium-sized cities were selected as the verification set using Landsat data. The training set, using the error matrix to systematically verify the extraction results; using four quantitative indicators of landscape shape index, aggregation index, edge area ratio and connectivity index to verify the shape and area similarity of the night light remote sensing data extraction results; random sampling 1014 sample points were extracted in the same way to verify the night light data extraction results in Zhejiang Province.

以往夜光遥感灯光区提取结果精度评价中存在部分问题:(1)验证集选取过少,分布方式不合理,与总体提取区域相比缺少代表性;(2)选取的验证评价指标过少、不全面,不能真是反映出提取结果;(3)评价方法不系统,仅仅与国家统计数值相对不能说明提取结果的空间分布问题。In the past, there were some problems in the accuracy evaluation of the extraction results of luminous remote sensing lighting areas: (1) The selection of the verification set was too small, the distribution method was unreasonable, and it was not representative compared with the overall extraction area; (2) The selected verification evaluation indicators were too few and not enough. It is comprehensive and cannot really reflect the extraction results; (3) The evaluation method is not systematic, and the comparison with the national statistical values cannot explain the spatial distribution of the extraction results.

中国专利文献CN201110230310.9,申请日20110812,专利名称为:一种分析景观特征对遥感分类图斑精度影响的方法,包括步骤一、获取数据,包括对原始影像进行数据标准化处理;步骤二、对步骤一获得的数据进行图像识别,包括分类和分类后处理,其中分类过程中包括有分类图斑的确定;步骤三、对步骤二进行识别得到的数据进行分类图斑与真值数据图斑之间的相关性计算,进行回归曲线拟合建立回归模型,其中包括表达景观特征的景观指数定义;步骤四、对步骤三建立的回归模型进行统计检验,同时对景观指数表达的分类误差进行评价。Chinese patent document CN201110230310.9, application date 20110812, the patent name is: a method for analyzing the impact of landscape features on the accuracy of remote sensing classification spots, including step 1, obtaining data, including performing data standardization on the original image; The data obtained in step 1 is subjected to image recognition, including classification and post-classification processing, wherein the classification process includes the determination of classification patterns; The correlation calculation among them is carried out by regression curve fitting to establish a regression model, which includes the definition of the landscape index expressing the landscape characteristics; step 4, the regression model established in step 3 is statistically tested, and at the same time, the classification error expressed by the landscape index is evaluated.

上述专利文献采用遥感分类的空间特征进行精度评价与分析的理论基础,采用遥感分类图斑的景观特征对分类精度进行的描述与表达,为与土地覆盖专题图相关的应用及研究提供依据与指导。但是关于一种利用二级抽样模型的夜光遥感分类精度评价方法,完成对夜光遥感数据的系统、有效地实现对大面积夜光遥感分类结果的精度评价,对夜光遥感城镇提取结果质量检验,能够解决了以往验证集过少,评价参数不全面的问题,达到了一种全面精确的验证结果。的技术方案则无相应的公开。The above-mentioned patent documents use the spatial characteristics of remote sensing classification as the theoretical basis for accuracy evaluation and analysis, and use the landscape characteristics of remote sensing classification maps to describe and express the classification accuracy, providing basis and guidance for the application and research related to land cover thematic maps . However, regarding a method for evaluating the classification accuracy of luminous remote sensing using a two-stage sampling model, the systematic and effective implementation of the accuracy evaluation of the classification results of luminous remote sensing for large areas, and the quality inspection of the extraction results of luminous remote sensing cities and towns can be solved. In the past, the problems of too few verification sets and incomplete evaluation parameters were solved, and a comprehensive and accurate verification result was achieved. There is no corresponding disclosure of the technical scheme.

综上所述,亟需一种利用二级抽样模型的夜光遥感分类精度评价方法,完成对夜光遥感数据的系统、有效地实现对大面积夜光遥感分类结果的精度评价,对夜光遥感城镇提取结果质量检验,能够解决了以往验证集过少,评价参数不全面的问题,达到了一种全面精确的验证结果。而目前关于这种方法还未见报道。To sum up, there is an urgent need for a method for evaluating the classification accuracy of luminous remote sensing using a two-stage sampling model, to complete the system of luminous remote sensing data, and effectively realize the accuracy evaluation of large-area luminous remote sensing classification results, and to extract the results of luminous remote sensing towns. Quality inspection can solve the problem of too few verification sets and incomplete evaluation parameters in the past, and achieve a comprehensive and accurate verification result. But there is no report about this method at present.

发明内容Contents of the invention

本发明的目的是针对现有技术中的不足,提供一种利用二级抽样模型的夜光遥感分类精度评价方法,完成对夜光遥感数据的系统、有效地实现对大面积夜光遥感分类结果的精度评价,对夜光遥感城镇提取结果质量检验,能够解决了以往验证集过少,评价参数不全面的问题,达到了一种全面精确的验证结果。The purpose of the present invention is to address the deficiencies in the prior art, to provide a method for evaluating the accuracy of luminous remote sensing classification using a two-stage sampling model, to complete the system for luminous remote sensing data, and to effectively realize the accuracy evaluation of large-area luminous remote sensing classification results , the quality inspection of night light remote sensing town extraction results can solve the problems of too few verification sets and incomplete evaluation parameters in the past, and achieve a comprehensive and accurate verification result.

为实现上述目的,本发明采取的技术方案是:For realizing above-mentioned object, the technical scheme that the present invention takes is:

一种利用二级抽样模型的中国陆地夜光遥感分类精度评价方法,其特征在于,该方法具体包括以下步骤:A method for evaluating the classification accuracy of China's terrestrial luminous remote sensing classification using a two-stage sampling model, characterized in that the method specifically includes the following steps:

步骤S1,在NPP/VIIRS系列数据实现对中国陆地区域的建成区提取,得到未验证的提取结果;Step S1, realize the extraction of built-up areas in China's land area from the NPP/VIIRS series data, and obtain unverified extraction results;

步骤S2,建立二级抽样模型,完成对评价区域的抽样,得到可信的检验数据集;Step S2, establishing a two-level sampling model, completing the sampling of the evaluation area, and obtaining a credible inspection data set;

步骤S3,使用误差矩阵验证方法,对夜光数据的初步提取进行分区验证,并最终得到全体夜光数据分类结果的精度评价。Step S3, using the error matrix verification method to perform partition verification on the preliminary extraction of luminous data, and finally obtain the accuracy evaluation of the classification results of all luminous data.

作为一种优选的技术方案,所述步骤S2包括以下步骤:As a preferred technical solution, the step S2 includes the following steps:

步骤S21:结合中国的东部沿海、中部、西部三大经济区域划分,在覆盖中国陆地的Landsat 8影像中抽取部分影像,分别得到三大区域内的抽样集,完成第一级抽样;Step S21: Combining the division of the three major economic regions of China's eastern coast, central and western regions, extract some images from the Landsat 8 images covering China's land, respectively obtain the sampling sets in the three major regions, and complete the first-level sampling;

步骤S22:分别在东部沿海、中部、西部三大区域对应的各景影像中选取检验像元,得到第二级抽样的结果集,完成第二级抽样;Step S22: Select the inspection pixels in the images corresponding to the three major regions of the eastern coast, the central region and the western region respectively to obtain the result set of the second-level sampling, and complete the second-level sampling;

步骤S23:于高精度的Landsat 8影像中提取验证像元的属性值,判断该像元属于夜光区或非夜光区。Step S23: Extract the attribute value of the verification pixel from the high-precision Landsat 8 image, and determine whether the pixel belongs to a luminous area or a non-luminous area.

作为一种优选的技术方案,所述步骤S21第一级抽样方法为:首先按照该级抽样检验模型,从覆盖中国大陆的Landsat 8影像中抽取幅图作为样本,在中国东部沿海、中部、西部三大经济区域中按不同的权值进行抽样,等到总体为n的图幅验证集,通过检验每个样本图幅的质量来推断整批图集的质量水平。As a preferred technical solution, the first-level sampling method in step S21 is as follows: first, according to the sampling inspection model of this level, extract a picture from the Landsat 8 image covering mainland China as a sample, and use it in the eastern coastal, central, and western regions of China. Sampling is carried out according to different weights in the three major economic regions, and the quality level of the entire batch of atlases is inferred by checking the quality of each sample map until the verification set of n maps is obtained.

作为一种优选的技术方案,所述步骤S22中还包括根据NDVI、NDBI对Landsat影像进行分类,在大范围内确定影像中的城市区域;同时对城镇和非城镇交界的区域采用目视解译的方式精确确定,最终在Landsat图像中达到精确的提取结果,得到第二级抽样。As a preferred technical solution, the step S22 also includes classifying the Landsat image according to NDVI and NDBI, and determining the urban area in the image in a large scale; at the same time, visual interpretation is used for the area at the junction of towns and non-towns The method is accurately determined, and finally the precise extraction result is achieved in the Landsat image, and the second-level sampling is obtained.

作为一种优选的技术方案,步骤S3中包括以下步骤:As a preferred technical solution, step S3 includes the following steps:

步骤S31:叠置矢量化的夜光遥感影像分类结果和二级抽样模型得到的验证集;步骤S32:使用误差矩阵验证方法,分别对三大区域进行分类结果的精度评价;步骤S33:结合三大区域的验证结果,进行反推计算,最终得到可信可靠全局分类精度评价验证结果。Step S31: Superimpose the vectorized luminous remote sensing image classification results and the verification set obtained by the two-stage sampling model; Step S32: Use the error matrix verification method to evaluate the accuracy of the classification results for the three major regions; Step S33: Combine the three major areas The verification results of the region are back-calculated, and finally the credible and reliable global classification accuracy evaluation verification results are obtained.

作为一种优选的技术方案,步骤S3中还通过选取总体精度、kappa系数、用户精度、生产者精度、错分误差、漏分误差参数,系统地评价夜光遥感提取结果。As a preferred technical solution, in step S3, the night light remote sensing extraction results are also systematically evaluated by selecting the parameters of overall accuracy, kappa coefficient, user accuracy, producer accuracy, misclassification error, and omission error.

作为一种优选的技术方案,所述步骤S3中将分别在中国三个经济区域中通过选取总体精度、kappa系数、用户精度、生产者精度、错分误差、漏分误差参数,系统地评价夜光遥感提取结果方法求得每个区域的夜光遥感提取精度,再结合每个区域的权值计算出中国陆地区域夜光遥感提取的精度结果。As a preferred technical solution, in the step S3, the night light will be systematically evaluated by selecting the overall accuracy, kappa coefficient, user accuracy, producer accuracy, misclassification error, and omission error parameters in the three economic regions of China. The method of remote sensing extraction results obtains the extraction accuracy of night light remote sensing in each region, and then calculates the accuracy result of night light remote sensing extraction in China's land area by combining the weights of each area.

本发明优点在于:The present invention has the advantage that:

1、本发明的一种利用二级抽样模型的中国陆地夜光遥感分类精度进行评价的方法,针对大区面积区域利用二级抽样的理论,从覆盖中国陆地区域的所有Landsat 8影像中,选取了数据适当、分布合理的验证数据,可有效的保障后续验证结果的可靠性。1, a kind of method of the present invention utilizes the Chinese land luminous remote sensing classification accuracy of two-level sampling model to evaluate, utilize the theory of two-level sampling for large area areas, from covering all Landsat 8 images of China's land area, select Appropriate and well-distributed verification data can effectively guarantee the reliability of subsequent verification results.

2、该评价方法选取误差矩阵进行统计分析,选取了总体精度(oa)、kappa系数、用户精度、生产者精度、错分误差、漏分误差作为评价参数,从不同角度、不用需求系统全面的对分类结果进行了评价。2. The evaluation method selects the error matrix for statistical analysis, and selects the overall accuracy (oa), kappa coefficient, user accuracy, producer accuracy, misclassification error, and omission error as evaluation parameters. From different angles, it does not require a comprehensive system The classification results were evaluated.

3、本方法针对以往夜灯验证研究较少,往往与国家统计数据进行数值对比的不足,从地学角度出发,在空间角度进行抽样采样,实现了更准确的精度评价。3. In view of the fact that there are few researches on the verification of night lights in the past, and the numerical comparison with national statistical data is often insufficient, this method starts from the perspective of geosciences and conducts sampling from the perspective of space to achieve a more accurate accuracy evaluation.

附图说明Description of drawings

附图1为本发明二级抽样验证方法的流程图。Accompanying drawing 1 is the flowchart of two-stage sampling verification method of the present invention.

附图2为本发明夜光遥感影像分类结果结果。Accompanying drawing 2 is the classification result of luminous remote sensing image of the present invention.

附图3为本发明中国陆地区域Landsat8分布图。Accompanying drawing 3 is the Landsat8 distribution map of the Chinese land area of the present invention.

附图4为本发明第一级抽样验证集分布图。Accompanying drawing 4 is the distribution diagram of the first-stage sampling verification set of the present invention.

附图5为本发明第二级抽样验证集分布图。Accompanying drawing 5 is the distribution chart of the second stage sampling verification set of the present invention.

具体实施方式detailed description

下面结合附图对本发明提供的具体实施方式作详细说明。The specific embodiments provided by the present invention will be described in detail below in conjunction with the accompanying drawings.

实施例1Example 1

夜光遥感数据相对于普通可见光遥感等数据,在对城镇提取时,只可作为一种间接的遥感数据。本研究针对以往夜光遥感应用中重应用轻验证的现象,对夜光遥感中夜光区域进行了提取,同时针对提取结果进行了完备的精度验证研究,最终提出了一整套完整的提取、研究模型。Compared with ordinary visible light remote sensing data, night light remote sensing data can only be used as an indirect remote sensing data when extracting cities and towns. Aiming at the phenomenon that in the past, the application of luminous remote sensing is more important than verification, this study extracts the luminous area in luminous remote sensing, and conducts a complete accuracy verification research on the extraction results, and finally proposes a complete set of extraction and research models.

本研究方法的流程图如图1,主要包括3部分组成:The flow chart of this research method is shown in Figure 1, which mainly consists of three parts:

(1)夜光遥感影像分类。(1) Classification of luminous remote sensing images.

采用支持向量机分类器对中国陆地NPP/VIIRS夜光遥感图像进行分类,得到精度待验证的夜光区和非夜光区两类分类结果;Using the support vector machine classifier to classify China's land NPP/VIIRS luminous remote sensing images, two classification results of luminous areas and non-luminous areas are obtained whose accuracy is to be verified;

(2)建立二级抽样模型。(2) Establish a secondary sampling model.

①结合中国的东部沿海、中部、西部三大经济区域划分,在覆盖中国陆地的Landsat 8影像中抽取部分影像,分别得到三大区域内的抽样集,完成第一级抽样;其中,三大经济区域为我国政府划分的东部沿海区域:辽宁、北京、天津、河北、江苏、浙江、福建、广东、台湾、香港、澳门、广西;中部区域:黑龙江、吉林、内蒙古、山西、河南、湖北、湖南、江西;西部区域:陕西、甘肃、宁夏、四川、贵州、云南、青海、新疆、西藏;①Combining the division of the three major economic regions of China's eastern coast, central and western regions, some images were extracted from the Landsat 8 images covering China's land, and the sampling sets in the three major regions were respectively obtained to complete the first-level sampling; among them, the three major economic regions The region is the eastern coastal region divided by the Chinese government: Liaoning, Beijing, Tianjin, Hebei, Jiangsu, Zhejiang, Fujian, Guangdong, Taiwan, Hong Kong, Macau, Guangxi; the central region: Heilongjiang, Jilin, Inner Mongolia, Shanxi, Henan, Hubei, Hunan , Jiangxi; Western Region: Shaanxi, Gansu, Ningxia, Sichuan, Guizhou, Yunnan, Qinghai, Xinjiang, Tibet;

由于夜光遥感影像中光照区域反映了人类的活动,因此一级抽样中模型需同时考虑空间和人口密度的因素。本研究结合空间面积、人口密度为每个区域设定一个权值,进行不同比例的抽样。Since the illuminated area in night-light remote sensing images reflects human activities, the model in the first-level sampling needs to consider both space and population density factors. In this study, a weight is set for each region in combination with spatial area and population density, and different proportions of samples are sampled.

覆盖中国陆地的Landsat 8影像按世界广角参照系统-2分布,覆盖中国陆地约有536景。The Landsat 8 images covering the land of China are distributed according to the World Wide Angle Reference System-2, covering about 536 scenes of the land of China.

②分别在东部沿海、中部、西部三大区域对应的各景影像中选取检验像元,得到第二级抽样的结果集,完成第二级抽样;② Select test pixels from the images of each scene corresponding to the three major regions of the eastern coast, the central region, and the west, and obtain the result set of the second-level sampling to complete the second-level sampling;

③于高精度的Landsat 8影像中提取验证像元的属性值,判断该像元属于夜光区或非夜光区;③ Extract the attribute value of the verification pixel from the high-precision Landsat 8 image, and judge whether the pixel belongs to the luminous area or the non-luminous area;

对于检验像元的属性确定,在高精度的Landsat 8影像中根据首先根据NDVI、NDBI对Landsat影像进行分类,在大范围内确定影像中的城市区域。同时对城镇和非城镇交界的区域采用目视解译的方式精确确定,最终在Landsat 8影像中达到精确的提取结果,判断该像元属于夜光区或非夜光区。For the determination of the attributes of the inspection pixels, in the high-precision Landsat 8 images, the Landsat images are first classified according to NDVI and NDBI, and the urban areas in the images are determined in a large scale. At the same time, the area at the junction of urban and non-urban areas is accurately determined by visual interpretation, and finally an accurate extraction result is achieved in the Landsat 8 image, and it is judged that the pixel belongs to the luminous area or the non-luminous area.

(3)分类结果精度评价。(3) Evaluation of classification result accuracy.

①叠置矢量化的夜光遥感影像分类结果和二级抽样模型得到的验证集;②使用误差矩阵验证方法,分别对三大区域进行分类结果的精度评价;③结合三大区域的验证结果,进行反推计算,最终得到可信可靠全局分类精度评价验证结果。具体模型设计和决策规则如下:① Overlay vectorized luminous remote sensing image classification results and the verification set obtained by the two-stage sampling model; ② Use the error matrix verification method to evaluate the accuracy of the classification results for the three major regions; ③ Combining the verification results of the three major regions, conduct The calculation is reversed, and finally a credible and reliable global classification accuracy evaluation verification result is obtained. The specific model design and decision-making rules are as follows:

(1)二级抽样模型(1) Secondary sampling model

本文二级模型抽样设计中,第一级抽样检验模型以覆盖中国大陆区域Landsat 8的“图幅”为抽样单元,第二级抽样检验模型以图幅内“灯光区和非灯光区面积”为抽样单元。In the sampling design of the second-level model in this paper, the first-level sampling inspection model takes the "map frame" covering Landsat 8 in mainland China as the sampling unit, and the second-level sampling inspection model takes the "light area and non-light area area" in the map frame as the sampling unit. sampling unit.

1)第一级抽样模型的选取1) Selection of the first-level sampling model

第一级抽样检验模型的抽样单元为“图幅”(Map)。在第一级抽样检验中,首先按照该级抽样检验模型,从覆盖中国大陆的Landsat 8影像中抽取n个幅图作为样本,通过检验每个样本图幅的质量来推断整批图集的质量水平。The sampling unit of the first-level sampling inspection model is "Map". In the first-level sampling inspection, firstly, according to the sampling inspection model of this level, n images are selected from the Landsat 8 images covering mainland China as samples, and the quality of the entire batch of atlases is inferred by inspecting the quality of each sample image Level.

由于夜光遥感影像中光照区域反映了人类的活动,因此一级抽样中模型需同时考虑空间和人口密度的因素。我国政府根据经济技术发展水平和地理位置相结合的原则,将全国划分为三大经济地带,即:东部沿海区域、中部区域、西部区域。本研究为每个区域设定一个权值λi,进行不同比例的抽样。Since the illuminated area in night-light remote sensing images reflects human activities, the model in the first-level sampling needs to consider both space and population density factors. According to the principle of combining the level of economic and technological development and geographical location, the Chinese government divides the country into three major economic zones, namely: the eastern coastal area, the central area, and the western area. In this study, a weight λ i is set for each region, and different proportions are sampled.

λi=θ(ai)·θ(di) (1)λ i =θ(a i )·θ(d i ) (1)

其中为θ(ai)该区域在全国陆地面积中的占比,θ(di)为该区域在人口密度中占比,每个区域所抽取的样本量为Among them, θ(a i ) is the proportion of this region in the national land area, θ(d i ) is the proportion of the population density of this region, and the sample size of each region is

ni=λi·N (2)n ii ·N (2)

其中N为按WRS-2系统覆盖中国大陆的所有Landsat 8的影像。Where N is all Landsat 8 images covering mainland China according to the WRS-2 system.

2)第二级抽样模型的选取2) Selection of the second-level sampling model

第二级抽样检验模型中将图像的像元(Pixel)被定义为抽样单元,在本级抽样检验模型中,样本图幅中的所有像元被看作检验总体,根据第二级抽样检验模型,选择适量的样本量n',通过对每一个检查单元的属性值(夜光或非夜光)进行检验,判断该图幅的质量。In the second-level sampling inspection model, the pixel (Pixel) of the image is defined as the sampling unit. In the current-level sampling inspection model, all pixels in the sample frame are regarded as the inspection population. According to the second-level sampling inspection model , select an appropriate sample size n', and judge the quality of the map frame by checking the attribute value (luminous or non-luminous) of each inspection unit.

3)Landsat 8/OLI影像验证集的获取3) Acquisition of Landsat 8/OLI image verification set

夜光遥感影像陆地灯光区域中的实际地物主要包括城镇灯光,因此Landsat 8中主要提取城镇区域进行验证研究。Landsat系列遥感数据波段信息丰富,合理有效的利用波段信息可快速准确地提取出建成区面积。对于一幅Landsat 8遥感图像,进行城镇区域提取时,根据首先根据NDVI、NDBI对Landsat影像进行分类,在大范围内确定影像中的城市区域。同时对城镇和非城镇交界的区域采用目视解译的方式精确确定,最终在Landsat 8/OLI影像中达到精确的提取结果。The actual ground objects in the land light area of night light remote sensing images mainly include town lights, so Landsat 8 mainly extracts town areas for verification research. Landsat series remote sensing data is rich in band information, reasonable and effective use of band information can quickly and accurately extract the area of built-up areas. For a Landsat 8 remote sensing image, when extracting urban areas, the Landsat images are first classified according to NDVI and NDBI, and the urban areas in the image are determined in a large scale. At the same time, the area at the junction of urban and non-urban areas is accurately determined by visual interpretation, and finally an accurate extraction result is achieved in the Landsat 8/OLI image.

(2)验证方法(2) Verification method

误差矩阵可有效的对遥感图像分类结果进行评价。本方案首先对分类结果矢量化,通过对矢量数据的叠置分析,借助二级抽样得到的验证集建立误差矩阵,并选取总体精度(oa)、kappa系数、用户精度、生产者精度、错分误差、漏分误差对其结果进行系统评价。The error matrix can effectively evaluate the classification results of remote sensing images. This program first vectorizes the classification results, and establishes the error matrix with the help of the verification set obtained by secondary sampling through the overlapping analysis of the vector data, and selects the overall accuracy (oa), kappa coefficient, user accuracy, producer accuracy, and misclassification The error and omission error were used to systematically evaluate the results.

误差矩阵的分布关系如表1,The distribution relationship of the error matrix is shown in Table 1.

表1:误差矩阵及其元素分布Table 1: Error matrix and its element distribution

其中ωi表示待验证(生产者)提取结果的分类类别,ωj表示验证(用户)集的分类类别,两者类别对应一致,ρij表示类别ωi被分为ωj的像元数,ρi+和ρ+i分别代表了生产者和用户在不用类别上的像元总数,m为所有验证像元的综合。Among them, ω i represents the classification category of the extraction result to be verified (producer), ω j represents the classification category of the verification (user) set, and the two categories correspond to each other, and ρ ij represents the number of pixels in which category ω i is divided into ω j , ρ i+ and ρ +i respectively represent the total number of pixels of producers and users in different categories, and m is the synthesis of all verification pixels.

从误差矩阵中可以得到重要的验证结果:Important verification results can be obtained from the error matrix:

总体精度(overall accuracy)是误差矩阵内主对角线元素之和(正确分类的个数)除以总的采样个数:The overall accuracy is the sum of the main diagonal elements in the error matrix (the number of correct classifications) divided by the total number of samples:

生产者精度(producer accuracy)和用户精度(user accuracy)可以表示某一单个类别的精度。生产者精度为某类别正确分类个数除以该类的总采样个数(该类的列总和):Producer accuracy and user accuracy can represent the accuracy of a single category. Producer accuracy is the number of correct classifications of a certain category divided by the total number of samples of this category (the sum of the columns of this category):

pai=ρii+i, (5)pa iii+i , (5)

而用户精度定义为正确分类的该类的个数除以分为该类的采样个数(该类的行总和):While user accuracy is defined as the number of correctly classified classes divided by the number of samples classified into that class (row sum of that class):

uai=ρiii+, (6)ua iiii+ , (6)

除了以上各种描述性的精度测量,还包括错分误差(commission error)和漏分误差(omission error):In addition to the various descriptive accuracy measurements above, commission errors and omission errors are also included:

cei=1-uai, (7)ce i =1-ua i , (7)

oei=1-pai, (8)oe i =1-pa i , (8)

另外在误差矩阵基础上利用各种统计分析技术可以用于比较不同的分类方法,其中最常用的是Kappa分析技术:In addition, using various statistical analysis techniques on the basis of the error matrix can be used to compare different classification methods, the most commonly used of which is the Kappa analysis technique:

(3)精度结果的反演(3) Inversion of accuracy results

分别在中国3个经济区域中利用(2)的方法得到的每个区域的夜光遥感提取精度ai,再结合每个区域的权值计算出中国陆地区域夜光遥感提取的精度结果A。Using the method (2) to obtain the night light remote sensing extraction accuracy a i of each area in the three economic regions of China, and then combine the weights of each area to calculate the accuracy result A of the night light remote sensing extraction in China's land area.

通过全局精度A,用户即可直观的得出该夜光图像的提取精度。Through the global accuracy A, the user can intuitively obtain the extraction accuracy of the night light image.

验证效果Verify the effect

(1)夜光遥感提取结果(1) Extraction results of luminous remote sensing

验证实验采用上文提及的夜光遥感数据,以SVM提取方法,将2016年夜光数据进行分类。本文借助envi5.2软件,使用其集成的SVM分类算法对夜光数据进行监督分类,得到夜光区和非夜光区两类分类信息,结果如图2:The verification experiment uses the night light remote sensing data mentioned above, and uses the SVM extraction method to classify the 2016 night light data. With the help of envi5.2 software, this paper uses its integrated SVM classification algorithm to supervise and classify luminous data, and obtain two types of classification information, luminous areas and non-luminous areas. The results are shown in Figure 2:

图2中紫色区域为提取得到的城镇区域(像元数512284),绿色区域为夜间没有光亮(像元数13339719)的区域,城镇区域占全国陆地总面积的3.7%。The purple area in Figure 2 is the extracted urban area (512284 pixels), the green area is the area without light at night (13339719 pixels), and the urban area accounts for 3.7% of the total land area of the country.

(2)二级抽样模型的建立(2) Establishment of the secondary sampling model

根据第一级抽样方式,从覆盖中国陆地的536景Landsat 8影像中(图3)抽取10%即54景影像作为验证集,样本分布按中国三大经济区域划分分布。表2为三大区域面积与人口的统计表:According to the first-level sampling method, 10% of the 536 Landsat 8 images (Fig. 3) covering the land of China are selected as the verification set, and the samples are distributed according to China's three major economic regions. Table 2 is the statistical table of the area and population of the three major regions:

表2三大区域面积与人口统计表Table 2 Area and population statistics of the three major regions

按公式(1)(2)可计算得到东部沿海区域、中部区域、西部区域三个区域的抽样比为8.94:6.13:4.03,考虑到抽样图幅的完整性,实际抽样中采用8:6:4的抽样比,各区域分别抽取24、18、12景影像,样本分布如图4所示:图4中黄色区域即为第一级抽样选取的影像位置和覆盖的区域。According to the formula (1) (2), the sampling ratio of the eastern coastal region, the central region and the western region can be calculated as 8.94:6.13:4.03. Considering the integrity of the sampling map, the actual sampling ratio is 8:6: With a sampling ratio of 4, 24, 18, and 12 scenes of images were sampled in each area, and the sample distribution is shown in Figure 4: The yellow area in Figure 4 is the image position and covered area selected by the first-level sampling.

根据第二级抽样检验模型,需获取检验像元和该像素的分类属性,本文利用光谱分析与边界目视识别的方法将抽样选取的54景影像分别进行城镇面积的提取,一景影像共有7701*7831个像元点,全部作为验证集进行验证。Landsat 8影像提取结果和夜光数据提取结果进叠置分析,叠置效果如图5,图中阴影区域为Landsat 8/OLI影像中提取所得的夜光区域,通过对比的参考数据分类结果和夜光遥感的分类结果可评判出夜光图像提取结果的优劣。According to the second-level sampling inspection model, it is necessary to obtain the inspection pixel and the classification attribute of the pixel. In this paper, the method of spectral analysis and boundary visual recognition is used to extract the urban area of the 54 scene images selected by sampling. There are 7701 images in one scene. *7831 pixel points are all used as the verification set for verification. Landsat 8 image extraction results and luminous data extraction results were overlaid and analyzed. The overlay effect is shown in Figure 5. The shaded area in the figure is the luminous area extracted from the Landsat 8/OLI image. By comparing the classification results of the reference data and the luminous remote sensing The classification results can judge the pros and cons of night light image extraction results.

(3)验证结果统计(3) Statistics of verification results

本研究采用误差矩阵的方法对夜光数据提取区域进行验证,根据两类型的提取面积(Km2)得到误差矩阵统计结果表3,提取结果中非夜光区域占比远远大于夜光区域,因此后续的评价中非夜光区域对结果的影像更大。In this study, the method of error matrix is used to verify the extraction area of luminous data. According to the two types of extraction areas (Km 2 ), the statistical results of the error matrix are obtained in Table 3. The proportion of non-luminous areas in the extraction results is much larger than that of luminous areas. Therefore, the follow-up The non-luminous areas in the evaluation have a greater impact on the results.

表3误差矩阵统计表(单位:Km2)Table 3 Statistical table of error matrix (unit: Km 2 )

根据公式(4)(9)可分别计算得到三个区域总体精度oa和kappa系数,结果如表4According to the formulas (4) (9), the overall accuracy oa and kappa coefficients of the three regions can be calculated respectively, and the results are shown in Table 4

表4:总体精度与kappa系数对比表(单位:%)Table 4: Comparison table of overall accuracy and kappa coefficient (unit: %)

用户精度、生产精度、错分误差、漏分误差结果如表4:The results of user accuracy, production accuracy, misclassification error, and omission error are shown in Table 4:

表4:评价参数对比表(单位:%)Table 4: Evaluation parameter comparison table (unit: %)

从验证结果可得出本次夜光提取结果的总体精度oa达精度较高,但kappa系数只有37.54%,两个评论参数的数值差别较大;特别的,分类结果中夜光区域的用户精度只有25.43%,错分误差达到了74.57%,说明夜光区的分类效果较差;另一方面,黑夜区域的用户精度达到了99.88%,错分误差0.12%,漏分误差6.98%,说明此区域的分类较为精确。From the verification results, it can be concluded that the overall accuracy of the luminous extraction results is relatively high, but the kappa coefficient is only 37.54%, and the values of the two comment parameters are quite different; in particular, the user accuracy of the luminous area in the classification results is only 25.43 %, the misclassification error reached 74.57%, indicating that the classification effect of the luminous area is poor; more precise.

由此判断本次夜光遥感提取结果中夜光区域的分类精度较低,黑夜区域的分类结果极高,因为夜光区域的面积远小于黑夜区域的面积,夜光区域在总体精度评价中所占的比例较低,使得总体精度oa较高;从kappa系数上看,本次夜光遥感分类结果的结果与验证集的相似性较低,主要表现在夜光区域的分类精度较低,大部分属性黑夜的区域被误分为了夜光区域。由此得出本次分类结果的总体精度较高,但其中夜光区域的精度低于非夜光区域的分类精度。Therefore, it can be judged that the classification accuracy of the noctilucent region in this luminous remote sensing extraction result is low, and the classification result of the dark night region is extremely high, because the area of the luminous region is much smaller than the area of the dark night region, and the proportion of the luminous region in the overall accuracy evaluation is relatively small. low, which makes the overall accuracy oa higher; from the kappa coefficient, the results of this luminous remote sensing classification result have a low similarity with the verification set, which is mainly reflected in the low classification accuracy of luminous areas, and most of the dark night areas are classified as It was mistakenly classified as a luminous area. It can be concluded that the overall accuracy of the classification results is relatively high, but the accuracy of the luminous area is lower than that of the non-luminous area.

以上所述仅是本发明的优选实施方式,应当指出,对于本技术领域的普通技术人员,在不脱离本发明方法的前提下,还可以做出若干改进和补充,这些改进和补充也应视为本发明的保护范围。The above is only a preferred embodiment of the present invention, it should be pointed out that for those of ordinary skill in the art, without departing from the method of the present invention, some improvements and supplements can also be made, and these improvements and supplements should also be considered Be the protection scope of the present invention.

Claims (7)

1.一种利用二级抽样模型的中国陆地夜光遥感分类精度评价方法,其特征在于,该方法具体包括以下步骤:1. A method for evaluating the classification accuracy of China's terrestrial luminous remote sensing classification utilizing a secondary sampling model, characterized in that the method specifically comprises the following steps: 步骤S1,在NPP/VIIRS系列数据实现对中国陆地区域的建成区提取,得到未验证的提取结果;Step S1, realize the extraction of built-up areas in China's land area from the NPP/VIIRS series data, and obtain unverified extraction results; 步骤S2,建立二级抽样模型,完成对评价区域的抽样,得到可信的检验数据集;Step S2, establishing a two-level sampling model, completing the sampling of the evaluation area, and obtaining a credible inspection data set; 步骤S3,使用误差矩阵验证方法,对夜光数据的初步提取进行分区验证,并最终得到全体夜光数据分类结果的精度评价。Step S3, using the error matrix verification method to perform partition verification on the preliminary extraction of luminous data, and finally obtain the accuracy evaluation of the classification results of all luminous data. 2.根据权利要求1所述的利用二级抽样模型的中国陆地夜光遥感分类精度评价方法,其特征在于,所述步骤S2包括以下步骤:2. the Chinese land luminous remote sensing classification accuracy evaluation method utilizing two-stage sampling model according to claim 1, it is characterized in that, described step S2 comprises the following steps: 步骤S21:结合中国的东部沿海、中部、西部三大经济区域划分,在覆盖中国陆地的Landsat 8影像中抽取部分影像,分别得到三大区域内的抽样集,完成第一级抽样;Step S21: Combining the division of the three major economic regions of China's eastern coast, central and western regions, extract some images from the Landsat 8 images covering China's land, respectively obtain the sampling sets in the three major regions, and complete the first-level sampling; 步骤S22:分别在东部沿海、中部、西部三大区域对应的各景影像中选取检验像元,得到第二级抽样的结果集,完成第二级抽样;Step S22: Select the inspection pixels in the images corresponding to the three major regions of the eastern coast, the central region and the western region respectively to obtain the result set of the second-level sampling, and complete the second-level sampling; 步骤S23:于高精度的Landsat 8影像中提取验证像元的属性值,判断该像元属于夜光区或非夜光区。Step S23: Extract the attribute value of the verification pixel from the high-precision Landsat 8 image, and determine whether the pixel belongs to a luminous area or a non-luminous area. 3.根据权利要求1所述的利用二级抽样模型的中国陆地夜光遥感分类精度评价方法,其特征在于,所述步骤S21第一级抽样方法为:首先按照该级抽样检验模型,从覆盖中国大陆的Landsat 8影像中抽取幅图作为样本,在中国东部沿海、中部、西部三大经济区域中按不同的权值进行抽样,等到总体为n的图幅验证集,通过检验每个样本图幅的质量来推断整批图集的质量水平。3. The Chinese land luminous remote sensing classification accuracy evaluation method utilizing two-level sampling model according to claim 1, characterized in that, the first-level sampling method of the step S21 is: at first according to the sampling inspection model of this level, from the coverage of China Take a map from the Landsat 8 image of the mainland as a sample, and sample according to different weights in the three major economic regions of China's eastern coastal, central, and western regions. Wait until the overall n-map verification set, and pass the inspection of each sample map to infer the quality level of the entire batch of atlases. 4.根据权利要求1所述的利用二级抽样模型的中国陆地夜光遥感分类精度评价方法,其特征在于,所述步骤S22中还包括根据NDVI、NDBI对Landsat影像进行分类,在大范围内确定影像中的城市区域;同时对城镇和非城镇交界的区域采用目视解译的方式精确确定,最终在Landsat图像中达到精确的提取结果,得到第二级抽样。4. The Chinese land luminous remote sensing classification accuracy evaluation method utilizing two-level sampling model according to claim 1, characterized in that, in the step S22, it also includes classifying Landsat images according to NDVI and NDBI, and determining in a large range The urban area in the image; at the same time, the urban and non-urban border area is accurately determined by visual interpretation, and finally the accurate extraction result is achieved in the Landsat image, and the second-level sampling is obtained. 5.根据权利要求1所述的利用二级抽样模型的中国陆地夜光遥感分类精度评价方法,其特征在于,步骤S3中包括以下步骤:5. the Chinese land luminous remote sensing classification accuracy evaluation method utilizing two-stage sampling model according to claim 1, is characterized in that, comprises the following steps in the step S3: 步骤S31:叠置矢量化的夜光遥感影像分类结果和二级抽样模型得到的验证集;Step S31: superimposing the vectorized night light remote sensing image classification results and the verification set obtained by the secondary sampling model; 步骤S32:使用误差矩阵验证方法,分别对三大区域进行分类结果的精度评价;Step S32: using the error matrix verification method to evaluate the accuracy of the classification results for the three major regions; 步骤S33:结合三大区域的验证结果,进行反推计算,最终得到可信可靠全局分类精度评价验证结果。Step S33: Combining the verification results of the three major areas, perform reverse calculation, and finally obtain a credible and reliable global classification accuracy evaluation verification result. 6.根据权利要求5所述的利用二级抽样模型的中国陆地夜光遥感分类精度评价方法,其特征在于,步骤S3中还通过选取总体精度、kappa系数、用户精度、生产者精度、错分误差、漏分误差参数,系统地评价夜光遥感提取结果。6. The Chinese land luminous remote sensing classification accuracy evaluation method utilizing two-stage sampling model according to claim 5, characterized in that, in step S3, by selecting overall accuracy, kappa coefficient, user accuracy, producer accuracy, misclassification error , Leakage error parameters, and systematically evaluate the night light remote sensing extraction results. 7.根据权利要求1所述的利用二级抽样模型的中国陆地夜光遥感分类精度评价方法,其特征在于,所述步骤S3中将分别在中国三个经济区域中通过选取总体精度、kappa系数、用户精度、生产者精度、错分误差、漏分误差参数,系统地评价夜光遥感提取结果方法求得每个区域的夜光遥感提取精度,再结合每个区域的权值计算出中国陆地区域夜光遥感提取的精度结果。7. the Chinese land luminous remote sensing classification accuracy evaluation method utilizing two-stage sampling model according to claim 1, it is characterized in that, in described step S3, will be respectively in three economic regions of China by selecting overall accuracy, kappa coefficient, User accuracy, producer accuracy, misclassification error, omission error parameters, systematically evaluate night light remote sensing extraction results method to obtain the night light remote sensing extraction accuracy of each region, and then calculate the night light remote sensing of China's land area by combining the weights of each region Extracted precision results.
CN201710488761.XA 2017-06-23 2017-06-23 Utilize the Chinese land noctilucence Classification in Remote Sensing Image Accuracy Assessment of two stage sampling model Pending CN107203790A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201710488761.XA CN107203790A (en) 2017-06-23 2017-06-23 Utilize the Chinese land noctilucence Classification in Remote Sensing Image Accuracy Assessment of two stage sampling model

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201710488761.XA CN107203790A (en) 2017-06-23 2017-06-23 Utilize the Chinese land noctilucence Classification in Remote Sensing Image Accuracy Assessment of two stage sampling model

Publications (1)

Publication Number Publication Date
CN107203790A true CN107203790A (en) 2017-09-26

Family

ID=59907782

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201710488761.XA Pending CN107203790A (en) 2017-06-23 2017-06-23 Utilize the Chinese land noctilucence Classification in Remote Sensing Image Accuracy Assessment of two stage sampling model

Country Status (1)

Country Link
CN (1) CN107203790A (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109670556A (en) * 2018-12-27 2019-04-23 中国科学院遥感与数字地球研究所 Global heat source heavy industry region recognizer based on fire point and noctilucence data
CN109740678A (en) * 2019-01-07 2019-05-10 上海海洋大学 Remote sensing image classification accuracy test method based on multi-level uneven spatial sampling
CN109753916A (en) * 2018-12-28 2019-05-14 厦门理工学院 A method and device for constructing a scale conversion model of normalized differential vegetation index

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005189099A (en) * 2003-12-25 2005-07-14 National Institute Of Information & Communication Technology Method and apparatus for removing noise in SAR data processing
CN103955583A (en) * 2014-05-12 2014-07-30 中国科学院城市环境研究所 Method for determining threshold value of urban built-up area extracted through nighttime light data
CN104820774A (en) * 2015-04-16 2015-08-05 同济大学 Space complexity based mapsheet sampling method
CN105279521A (en) * 2015-09-28 2016-01-27 上海海洋大学 Remote-sensing image classification result precision examination method based on space sampling
CN106127121A (en) * 2016-06-15 2016-11-16 四川省遥感信息测绘院 A kind of built-up areas intellectuality extracting method based on nighttime light data

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005189099A (en) * 2003-12-25 2005-07-14 National Institute Of Information & Communication Technology Method and apparatus for removing noise in SAR data processing
CN103955583A (en) * 2014-05-12 2014-07-30 中国科学院城市环境研究所 Method for determining threshold value of urban built-up area extracted through nighttime light data
CN104820774A (en) * 2015-04-16 2015-08-05 同济大学 Space complexity based mapsheet sampling method
CN105279521A (en) * 2015-09-28 2016-01-27 上海海洋大学 Remote-sensing image classification result precision examination method based on space sampling
CN106127121A (en) * 2016-06-15 2016-11-16 四川省遥感信息测绘院 A kind of built-up areas intellectuality extracting method based on nighttime light data

Non-Patent Citations (8)

* Cited by examiner, † Cited by third party
Title
KAIFANG SHI ET AL: "Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas", 《REMOTE SENSING LETTERS》 *
WENKAI LI ET AL: "A New Accuracy Assessment Method for One-Class Remote Sensing Classification", 《IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING》 *
孟雯等: "基于空间抽样的区域地表覆盖遥感制图产品精度评估—以中国陕西省为例", 《地球信息科学》 *
李松: "《乌鲁木齐居住空间分异及响应研究》", 28 February 2017 *
柴宝惠等: "基于Landsat 数据和DMSP/OLS 夜间灯光数据的城市扩展提取: 以天津市为例", 《北京大学学报(自然科学版)》 *
梁保平等: "桂林市主城区建设用地扩张时空动态特征分析", 《广西师范大学学报:自然科学版》 *
王振华: "空间数据质量抽样检验与控制的理论、方法和应用", 《国家图书馆》 *
陈珂: "遥感影像分类结果的空间抽样精度检验方法研究", 《中国优秀硕士学位论文全文数据库 信息科技辑》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109670556A (en) * 2018-12-27 2019-04-23 中国科学院遥感与数字地球研究所 Global heat source heavy industry region recognizer based on fire point and noctilucence data
CN109670556B (en) * 2018-12-27 2023-07-04 中国科学院遥感与数字地球研究所 Identification method of global heat source and heavy industry region based on fire point and night light data
CN109753916A (en) * 2018-12-28 2019-05-14 厦门理工学院 A method and device for constructing a scale conversion model of normalized differential vegetation index
CN109740678A (en) * 2019-01-07 2019-05-10 上海海洋大学 Remote sensing image classification accuracy test method based on multi-level uneven spatial sampling

Similar Documents

Publication Publication Date Title
CN110321815A (en) A kind of crack on road recognition methods based on deep learning
CN109446992B (en) Remote sensing image building extraction method and system based on deep learning, storage medium and electronic equipment
CN107230197B (en) An objective method for determining the intensity of tropical cyclones based on satellite cloud images and RVM
Li et al. SPM-IS: An auto-algorithm to acquire a mature soybean phenotype based on instance segmentation
CN108776777A (en) The recognition methods of spatial relationship between a kind of remote sensing image object based on Faster RCNN
CN111178392B (en) Aero-engine hole detection image damage segmentation method based on deep neural network
CN103617336B (en) A kind of method for drafting of aircraft noise isogram
Wang et al. Detection of asphalt pavement cracks based on vision transformer improved YOLO V5
CN107203790A (en) Utilize the Chinese land noctilucence Classification in Remote Sensing Image Accuracy Assessment of two stage sampling model
Wang et al. Insulator defect recognition based on faster R-CNN
CN111639530A (en) Detection and identification method and system for power transmission tower and insulator of power transmission line
CN114898089A (en) Functional area extraction and classification method integrating high-resolution images and POI data
Wang et al. Automatic identification and location of tunnel lining cracks
Dai et al. Understanding the abrupt climate change in the mid-1970s from a phase-space transform perspective
CN105740901A (en) Geographic ontology based variable scale object-oriented remote sensing classification correction method
Li et al. The extraction of built-up areas in Chinese mainland cities based on the local optimal threshold method using NPP-VIIRS images
CN104809336B (en) A kind of regional feature sampling approach for considering spatial coherence
CN117372710A (en) Forest gap extraction method based on Sentinel-2MSI remote sensing image
CN113591668B (en) Wide area unknown dam automatic detection method using deep learning and space analysis
Liu et al. Recognition and quantification of apparent damage to concrete structure based on computer vision
Chen et al. The Diverse Mountainous Landslide Dataset (DMLD): A High-Resolution Remote Sensing Landslide Dataset in Diverse Mountainous Regions
Zhang et al. Solar photovoltaic module defect detection based on deep learning
Wang et al. Physical Urban Area Identification Based on Vector Buildings and Quantitative Attribution of Optimal Distance Threshold: A Case Study in Chongqing Municipality, Southwestern China
CN114648074B (en) Loess cave-trapping object-oriented extraction method integrating topographic features
Zhang et al. Research and implementation of key intelligent detection technologies based on deep learning

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication

Application publication date: 20170926

RJ01 Rejection of invention patent application after publication