Variability analysis and change characterization of HSI data for urban mapping

Urban areas are highly variable in remote sensing data, thus it can be difficult to detect changes over time caused by development or by movement of specific targets. This research is a first attempt at exploration of repeat flights of 20m spatial resolution imaging spectrometer data to identify and characterize sources and the nature of spectral variability in urban/rural environments. Data for two dates were atmospherically corrected using a MODTRAN-based model to independently correct each dataset for atmospheric effects. The data were geocorrected and co-registered then cropped to a common spatial coverage. Spectra for individual pixels and for ROIs were extracted from the two datasets and visual and statistical comparisons were made between spectra to assess the effects of collection parameters and atmospheric corrections. Spectral endmembers were determined for each flightline, mapped using spectral matching methods, and compared across flightlines. This approach allowed determination of relatively spectrally invariant areas versus areas with significant spectral change. The combined datasets were used to develop thematic layers and evaluate spectral variability versus changes principally due to urban development.