Multitemporal land-use analysis is becoming increasingly important for the effective management of Earth resources. Despite that, consistent differences in the viewing and illumination geometry in satellite-borne imagery introduce some issues in the creation of land-use classification maps. The focus of this letter is settlement classification with high-resolution panchromatic acquisitions, using texture features to distinguish between settlement classes. The important multitemporal variance component of shadow is effectively removed before feature determination, which allows for minimum-supervision across-date classification. Shadow detection based on local adaptive thresholding is employed and experimentally shown to outperform existing fixed threshold shadow detectors in increasing settlement classification accuracy. Both same- and across-date settlement accuracies are significantly improved with shadow masking during feature calculation. A statistical study was performed and found to support the hypothesis that the increased accuracy is due to shadow masking specifically.
Reference:
Luus, F.P.S, Van den Bergh, F and Maharaj, B.T.J. 2014. Adaptive threshold-based shadow masking for across-date settlement classification of panchromatic quickBird images. IEEE Geoscience and Remote Sensing Letters, vol. 2(6), pp 1153-1157
Luus, F., Van den Bergh, F., & Maharaj, B. (2014). Adaptive threshold-based shadow masking for across-date settlement classification of panchromatic quickBird images. http://hdl.handle.net/10204/7361
Luus, FPS, F Van den Bergh, and BTJ Maharaj "Adaptive threshold-based shadow masking for across-date settlement classification of panchromatic quickBird images." (2014) http://hdl.handle.net/10204/7361
Luus F, Van den Bergh F, Maharaj B. Adaptive threshold-based shadow masking for across-date settlement classification of panchromatic quickBird images. 2014; http://hdl.handle.net/10204/7361.
Copyright: 2014 IEEE Xplore. This is an ABSTRACT ONLY. The definitive version is published in IEEE Geoscience and Remote Sensing Letters, vol. 2(6), pp 1153-1157