Abstract
In digital satellite imagery, small fragments of woody vegetation are difficult to detect because they frequently are smaller than the pixel size and are mixed with other land cover classes. A method for detecting subpixel woody vegetation, which analyzes mixture phenomena at the individual pixel level, is presented. This method relies on a moving window to collect training sets for adjacent land cover. In order to locate pixels of interest and to decrease noise, image-derived masks are integrated with the original digital imagery in a geocoded information system. A rule-based scheme is employed to organize relative spatial and spectral information into classification decision procedures. Tests using simulated multispectral and panchromatic SPOT HRV imagery of lowland Britain have shown that the developed method discriminates significantly more woody vegetation than standard multispectral classification.
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