An investigation of the role of image properties in influencing the accuracy of remote sensing change detection analysis.

摘要

The purpose of this study was to examine the influence of image properties on the accuracy of remote sensing change detection methods. Spectral class separability, radiometric normalization and image band correlation were evaluated through experiments with simulated data. The experimental results were then evaluated as a tool for predicting the relative accuracy of change detection results obtained from Landsat TM satellite image pairs of three U.S. cities: Las Vegas, Nevada; Phoenix, Arizona; and Atlanta, Georgia. The change detection methods used were post-classification comparison, direct classification, image differencing, principal component analysis, and change vector analysis. Results of the simulated experiments confirmed that the relative accuracy of the change detection methods varied with changes in image properties. For the class separability experiments, post-classification comparison, direct classification, image differencing, and PCA with a large number of the principal components, were found generally to have higher accuracies than CVA and PCA with a small number of the principal components. For classes with very good separability, image differencing is an excellent method; for classes with poor spectral separability, image differencing was found to have the lowest accuracy. The influence of the error in radiometric normalization on the accuracy of change detection techniques varied greatly with different degrees of class separability. This can be seen particularly well with image differencing, which showed the highest sensitivity to large changes in radiometric error and very poor class separability. Image differencing and PCA were also found to be more sensitive to band correlation. The classification of the real change detection data from the Landsat pairs showed complex and varying patterns, depending on whether complete (mapping all unchanged and changed transitions) or partial (grouping all unchanged transitions into a single class) change analysis was conducted. However, image differencing was relatively consistent in producing good results for the real data. Also, PCA produced satisfactory results for all three cities. On the other hand, the CVA was found to have amongst the lowest accuracies of the partial change detection methods, though this was higher than most of the complete change accuracy. The variation of accuracy results obtained from the change detection methods used in this study suggests that the contradictory results found in previous change detection studies is likely at least partially a result of varying image properties.