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Remote Sens. 2010, 2(6), 1508-1529; doi:10.3390/rs2061508
Article

Change Detection Accuracy and Image Properties: A Study Using Simulated Data

1 and 2,*
1 Geography Department, Imam Mohammad bin Saud University, Riyadh, Saudi Arabia 2 Department of Geology and Geography, West Virginia University, Morgantown, WV 26506, USA
* Author to whom correspondence should be addressed.
Received: 14 April 2010 / Revised: 27 May 2010 / Accepted: 2 June 2010 / Published: 3 June 2010
(This article belongs to the Special Issue Multi-Temporal Remote Sensing)
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Abstract

Simulated data were used to investigate the relationships between image properties and change detection accuracy in a systematic manner. The image properties examined were class separability, radiometric normalization and image spectral band-to-band correlation. The change detection methods evaluated were post-classification comparison, direct classification of multidate imagery, image differencing, principal component analysis, and change vector analysis. The simulated data experiments showed that the relative accuracy of the change detection methods varied with changes in image properties, thus confirming the hypothesis that caution should be used in generalizing from studies that use only a single image pair. In most cases, direct classification and post-classification comparison were the least sensitive to changes in the image properties of class separability, radiometric normalization error and band correlation. Furthermore, these methods generally produced the highest accuracy, or were amongst those with a high accuracy. PCA accuracy was highly variable; the use of four principal components consistently resulted in substantial decreased classification accuracy relative to using six components, or classification using the original six bands. The accuracy of image differencing also varied greatly in the experiments. Of the three methods that require radiometric normalization, image differencing was the method most affected by radiometric error, relative to change vector and classification methods, for classes that have moderate and low separability. For classes that are highly separable, image differencing was relatively unaffected by radiometric normalization error. CVA was found to be the most accurate method for classes with low separability and all but the largest radiometric errors. CVA accuracy tended to be the least affected by changes in the degree of band correlation in situations where the class means were moderately dispersed, or clustered near the diagonal. For all change detection methods, the classification accuracy increased as simulated band correlation increased, and direct classification methods consistently had the highest accuracy, while PCA generally had the lowest accuracy.
Keywords: change detection; post-classification comparison; change classification; image differencing; principal component analysis; change vector analysis; simulated data change detection; post-classification comparison; change classification; image differencing; principal component analysis; change vector analysis; simulated data
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Almutairi, A.; Warner, T.A. Change Detection Accuracy and Image Properties: A Study Using Simulated Data. Remote Sens. 2010, 2, 1508-1529.

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