The Effects of Segmentation-Based Shadow Removal on Across-Date Settlement Type Classification of Panchromatic QuickBird Images

Settlement classifiers for multitemporal satellite image analysis have to overcome numerous difficulties related to across-date variances in viewing- and illumination geometry. Shadow anisotropy is a prominent contributing factor in classifier inaccuracy, so methods are introduced in this study to enable minimum-supervision classifier design that mitigate the effects of shadow profile differences. A segmentation-based shadow detector is proposed that utilizes a panchromatic segment merging algorithm with parameters that are robust against dynamic range variances seen in multitemporal imagery. The proposed shadow detector improves on the settlement classification accuracy achieved by fixed threshold detection paired with shadow removal in the presented case-study. The relationship between shadow detection accuracy and settlement classification accuracy is investigated, and it is shown that shadow removal produces greater settlement accuracy improvements for across-date experiments specifically.