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Four atmospheric correction methods, two relative and two absolute, were compared in this study. Two of the methods (PIF and RCS) were relative approaches; COST is an absolute image-based method and 6S, an absolute modeling method. The methods were applied to the hazy bands 1 through 4 of a Landsat TM scene of the year 1997, which was being used in a change detection project. The effects of corrections were studied in woodland patches. Three criteria, namely (a) image attributes; (b) image classification results, and (c) landscape metrics, were used for comparing the performance of the correction methods. Average pixel values, dynamic range, and coefficient of variation of bands constituted the first criterion, the area of detected vegetation through image classification was the second criterion, and patch and landscape measures of vegetation the third criterion. Overall, the COST, RCS, and 6S methods performed better than PIF and showed more stable results. The 6S method produced some negative values in bands 2 through 4 due to the unavailability of some data needed in the model. Having to use only a single set of image pixels for normalization in the PIF method and the difficulty of selecting such samples in the study area may be the reasons for its poor performance.
Mahiny et al. (Sun,) studied this question.