NDLI: A Markov Chain Geostatistical Framework for Land-Cover Classification With Uncertainty Assessment Based on Expert-Interpreted Pixels From Remotely Sensed Imagery

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A Markov Chain Geostatistical Framework for Land-Cover Classification With Uncertainty Assessment Based on Expert-Interpreted Pixels From Remotely Sensed Imagery

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A Markov Chain Geostatistical Framework for Land-Cover Classification With Uncertainty Assessment Based on Expert-Interpreted Pixels From Remotely Sensed Imagery

Content Provider	IEEE Xplore Digital Library
Author	Weidong Li Chuanrong Zhang
Copyright Year	1980
Abstract	This paper introduces an expert interpretation-based Markov chain geostatistical (MCG) framework for classifying land-use/land-cover (LULC) classes from remotely sensed imagery. The framework uses the MCG method to classify uninformed pixels based on the informed pixels and quantify the associated uncertainty. The method consists of the following steps: 1) decide the number of LULC classes and define the physical meaning of each class; 2) obtain a data set of class labels from one or a time series of remotely sensed images through expert interpretation; 3) estimate transiogram models from the data set; and 4) use the Markov chain sequential simulation algorithm to conduct simulations that are conditional to the data set. The simulated results not only provide classified LULC maps but also quantify the uncertainty associated with the classification. A case study with three LULC classes shows that, with increasing number of informed pixels from 0.45% to 1.81% of the total pixels at the resolution of 4.8 m × 4.8 m, the optimal classification accuracy based on maximum probabilities increases from 88.13% to 99.23% and the averaged classification accuracy of realization maps increases from 81.84% to 97.18%. Although it is relatively labor intensive, such an expert interpretation and geostatistical simulation-based approach may provide a useful LULC classification method complementary to existing image processing methods, which usually account for limited expert knowledge and may not incorporate ground observation data or assess the uncertainty associated with classified data.
Sponsorship	IEEE Geoscience and Remote Sensing Society IEEE URSI
Starting Page	2983
Ending Page	2992
Page Count	10
File Size	717930
File Format	PDF
ISSN	01962892
Volume Number	49
Issue Number	8
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pixel Markov processes Remote sensing Data models Uncertainty Mathematical model Correlation uncertainty Classification algorithms expert interpretation geostatistics land cover Markov chain remotely sensed image
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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