Abstract
Mapping surface sediment types is particularly challenging in muddy intertidal flat area due to muddy characteristics and
tidal fluctuation. With the combination of Hyperion hyperspectral image and field survey data, two regression based
image interpretation methods, namely characteristic band method (CBM) and band differential method (BDM), were
used for sediment type classification and mapping. It was found that under low tidal level there was a strong correlation
between surface sediment reflectance and its sand, silt and clay contents in shortwave infrared band. For 2102nm
wavelength, the correlation coefficient by former method reached -0.8954, 0.9070 and 0.6547 respectively while the
latter method had a relatively lower correlation capability. So choosing this band as the characteristic band, three linear
regression models were constructed and the sand, silt and clay contents were quantitatively inversed from their
corresponding reflectance values. A linear equilibrium corrective method was then applied to some "bad" pixels for
inversed contents amendment due to regression model's linear transforming limitation. Based on these corrected
component contents, Shepard triangular classification method was adopted and the sediment types for the whole
intertidal flat were automatically obtained with a high interpretation precision of 87.9%. Results showed that the
hyperspectral remote sensing reversion method could be well utilized for dynamic monitoring and analyzing of the
depositional environment changes in muddy intertidal flat region.
