NDLI: Quantification of the Effects of Land-Cover-Class Spectral Separability on the Accuracy of Markov-Random-Field-Based Superresolution Mapping

Content Provider	IEEE Xplore Digital Library
Author	Tolpekin, V.A. Stein, A.
Copyright Year	1980
Abstract	This paper explores the effects of class separability in Markov-random-field-based superresolution mapping (SRM). We propose to account for class separability by means of controlling the balance tuned by a smoothness parameter between the prior and the likelihood terms in the posterior energy function. A generally applicable procedure estimates the optimal smoothness parameter, based on local energy balance analysis. The study shows how the optimal value of the smoothness parameter depends quantitatively and monotonically upon the class separability and the scale factor. Effects are studied on an image synthesized from an agricultural scene with field boundary subpixels. We varied systematically the class separability, the scale factor, and the smoothness parameter values. The accuracy of the resulting land-cover-map image is assessed by means of the kappa statistic at the fine-resolution scale and the class area proportion at the coarse-resolution scale. Performance is compared with a hard and a soft classification of the coarse-resolution image. We demonstrate that an optimal value of the smoothness parameter exists for each combination of scale factor and class separability. This allows us to reach a high classification accuracy (kappa = 0.85) even for poorly separable classes, i.e., with a transformed divergence equal to 0.5 and a scale factor equal to 10. The developed procedure agrees with the empirical data for the optimal smoothness parameter. The study shows that SRM is now applicable to a larger set of images with class separability ranging from poor to excellent.
Sponsorship	IEEE Geoscience and Remote Sensing Society IEEE URSI
Starting Page	3283
Ending Page	3297
Page Count	15
File Size	655800
File Format	PDF
ISSN	01962892
Volume Number	47
Issue Number	9
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Spatial resolution Image resolution Image sensors Sensor phenomena and characterization Remote sensing Image classification Markov random fields Vegetation mapping Geoscience Energy resolution superresolution mapping (SRM) Class separability image classification Markov random field (MRF)
Content Type	Text
Resource Type	Article
Subject	Earth and Planetary Sciences Electrical and Electronic Engineering

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