A Comparative Study of Land Cover Classification by Using Multispectral and Texture Data.
The main objective of this study is to find out the importance of machine vision approach for the classification of five types of land cover data such as bare land, desert rangeland, green pasture, fertile cultivated land, and Sutlej river land. A novel spectra-statistical framework is designed to c...
| Publicado en: | BioMed Research International Vol. 2016; pp. 1 - 13 |
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| Autores principales: | , , , , , , , , , , , , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
6/8/2016
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=115985900&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 115985900 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 6/8/2016 vid: 2016 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 115985900 115985900 115985900 10.1155/2016/8797438 115985900 ppf: 1 ppct: 12 formats: fmt: @attributes: type: P tig: atl: A Comparative Study of Land Cover Classification by Using Multispectral and Texture Data. aug: au: Qadri, Salman Khan, Dost Muhammad Ahmad, Farooq Qadri, Syed Furqan Babar, Masroor Ellahi Shahid, Muhammad Ul-Rehman, Muzammil Razzaq, Abdul Shah Muhammad, Syed Fahad, Muhammad Ahmad, Sarfraz Pervez, Muhammad Tariq Naveed, Nasir Aslam, Naeem Jamil, Mutiullah Rehmani, Ejaz Ahmad Ahmad, Nazir Akhtar Khan, Naeem affil: Department of CS & IT, The Islamia University of Bahawalpur, Punjab 63100, Pakistan sug: subj: Natural Environment Classification Comparative Studies Pakistan Descriptive Statistics Digital Imaging Discriminant Analysis Neural Networks (Computer) Telemetry Methods Radiometry Methods Data Analysis Software Correlation Coefficient Validity ab: The main objective of this study is to find out the importance of machine vision approach for the classification of five types of land cover data such as bare land, desert rangeland, green pasture, fertile cultivated land, and Sutlej river land. A novel spectra-statistical framework is designed to classify the subjective land cover data types accurately. Multispectral data of these land covers were acquired by using a handheld device named multispectral radiometer in the form of five spectral bands (blue, green, red, near infrared, and shortwave infrared) while texture data were acquired with a digital camera by the transformation of acquired images into 229 texture features for each image. The most discriminant 30 features of each image were obtained by integrating the three statistical features selection techniques such as Fisher, Probability of Error plus Average Correlation, and Mutual Information (F + PA + MI). Selected texture data clustering was verified by nonlinear discriminant analysis while linear discriminant analysis approach was applied for multispectral data. For classification, the texture and multispectral data were deployed to artificial neural network (ANN: n-class). By implementing a cross validation method (80-20), we received an accuracy of 91.332% for texture data and 96.40% for multispectral data, respectively. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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