城市边界线预测（根据灯光指数）(PUL)

Posted 2021-02-09 2008nmj

1.EXEALL.m

function EXEALL(FilePath, FileName)
%执行所有流程
% FilePath: 文件夹所在路径
% FileName: 文件夹名称

FullPath = [FilePath , FileName , ‘‘ , FileName , ‘.txt‘];
allPixelPath = [FilePath , FileName , ‘‘, FileName , ‘.allpixel.csv‘];
TestdataPath = [FilePath , FileName , ‘dataUrban.Test.csv‘];
TraindataPath = [FilePath , FileName , ‘dataUrban.Train.csv‘];
netPath = [FilePath , FileName , ‘ etUrban.net.mat‘];
predictFld = [FilePath , FileName , ‘predict‘];
accFld = [FilePath , FileName , ‘acc‘];
FileFld = [FilePath , FileName , ‘‘];

if ~exist(allPixelPath,‘file‘)
allpixel(FullPath,allPixelPath);
end
if ~exist(TraindataPath,‘file‘)
FormatData(allPixelPath, TestdataPath, TraindataPath,4000,5000,1000);
end
if exist(TraindataPath,‘file‘)
pbl_train(TraindataPath,netPath);
end
if exist(netPath,‘file‘)
TestDataPredict(TestdataPath,netPath,predictFld,FileName);
TestDataAcc(TestdataPath,predictFld,accFld,FileName);
PredictAll(allPixelPath,netPath,FileFld,FileName)
end
end

 2. 

Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data

Abstract: The accurate and timely monitoring of regional urban extent is helpful for analyzing urban sprawl and studying environmental issues related to urbanization. This paper proposes a classification scheme for large-scale urban extent mapping by combining the Day/Night Band of the Visible Infrared Imaging Radiometer Suite on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS DNB) and the Normalized Difference Vegetation Index from the Moderate Resolution Imaging Spectroradiometer products (MODIS NDVI). A Back Propagation (BP) neural network based one-class classification method, the Present-Unlabeled Learning (PUL) algorithm,is employed to classify images into urban and non-urban areas. Experiments are conducted in mainland China (excluding surrounding islands) to detect urban areas in 2012. Results show that the proposed model can successfully map urban area with a kappa of 0.842 on the pixel level. Most of the urban areas are identified with a producer’s accuracy of 79.63%, and only 10.42% the generated urban areas are misclassified with a user’s accuracy of 89.58%. At the city level, among 647 cities, only four county-level cities are omitted. To evaluate the effectiveness of the proposed scheme, three contrastive analyses are conducted: (1) comparing the urban map obtained in this paper with that generated by
the Defense Meteorological Satellite Program/Operational Linescan System Nighttime Light Data (DMSP/OLS NLD) and MODIS NDVI and with that extracted from MCD12Q1 in MODIS products;(2) comparing the performance of the integration of NPP-VIIRS DNB and MODIS NDVI with single input data; and (3) comparing the classification method used in this paper (PUL) with a linear method(Large-scale Impervious Surface Index (LISI)). According to our analyses, the proposed classification scheme shows great potential to map regional urban extents in an effective and efficient manner.
Keywords: urban mapping; one-class; NPP-VIIRS DNB; MODIS NDVI; large scale