Per-pixel classification of high spatial resolution satellite imagery for urban land-cover mapping

Commercial high spatial resolution satellite data now provide a synoptic and consistent source of digital imagery with detail comparable to that Of aerial photography. In the work described here, per-pixel classification, image fusion, and GIS-based map refinement techniques were tailored to pan-sharpened 0.61 m QuickBird imagery to develop a six-category urban land-cover map with 89.3 percent overall accuracy (kappa = 0.87). The study area was a rapidly developing 71.5 km(2) part of suburban Raleigh, North Carolina, U.S.A., within the Neuse River basin,'Edge pixels were a source of classification error as was spectral overlap between bare soil and impervious surfaces and among vegetated cover types. Shadows were not a significant source of classification error, These findings demonstrate that conventional spectral-based classification methods can be used to generate highly accurate maps of urban landscapes using high spatial resolution imagery.