Abstract:
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 study 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.
