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

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 OPEN ACCESSRemote Sens. 2010, 2 1509 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.

Landsat TM imagery and NDVI differencing to detect vegetation change: assessing natural forest expansion in Basilicata, southern Italy

“…Jomaa & Bou Keir (2003) reported anthropogenic land use change detection with the trial and test approach, and determined 2·σ was the optimal threshold value for two sites in Lebanon. Yacouba et al (2009) (Metternicht 1999, Bruzzone & Fernandez Prieto 2000a, 2000b, Hodgson et al 2004, Im et al 2007, Boone et al 2007, Almutairi & Warner 2010. Nevertheless, due to its simplicity, the image thresholding method based on standard deviation remains the most commonly used method for the detection in vegetation changes.…”

Generation of Radiometric, Phenological Normalized Image Based on Random Forest Regression for Change Detection

“…The obtained R 2 and RMSE values are shown in Table 2. The mean R 2 values of MS regression, SR, and NC regression were improved by 0.12%, 0.01%, and 0.13%, respectively, and the mean RMSE values were improved by 25.61%, 33.22%, and 24.93%, respectively, relative to the raw subject images.…”

“…For a comparison of the proposed method, R 2 and RMSE values were computed between normalized subject images generated by linear regression and reference images. The obtained R 2 …”

“…Change detection techniques using satellite remote sensing images are methods to quantitatively analyze changes occurring to a targeted area based on data obtained for two different points in time [1][2][3][4]. As multi-temporal images are often captured by different sensors under variable atmospheric conditions, degrees of solar illumination, and viewing angles, radiometric normalization is required to remove radiometric differences and make the images comparable [5][6][7].…”

Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images

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