Spatial and spectral classification of remote-sensing imagery

Abstract The use of spatial satellite image information and digital elevation models in remote-sensing classification is described for a mountainous region in southwestern Yukon. A three-stage classification method was devised that incorporates a quadtree-based segmentation operator, a Gaussian minimum distance to means test, and a final test involving ancillary topographic data and a spectral curve measure. The overall improvement in accuracy is significant compared to simple multispectral techniques, and the resulting map products are consistent with few unclassified areas. The three-stage classifier can produce an output map in significantly less time than that required for per-pixel maximum likelihood classifiers, and uses a minimum of field or training data which may be difficult and expensive to acquire in complex terrain. Programs to handle spatial and spectral attributes are coded efficiently in the C programming language. They can be adapted to locate homogeneous regions in high resolution aerial imaging spectrometer data sets (down to 0.1 m pixel resolution) or other raster databases.