An assessment of two classification methods for mapping Thames Estuary intertidal habitats using CASI data

The aim of this study was to find the most appropriate method of classification for the Thames intertidal habitat types at Crayford Marsh and Dartford Creek by using Compact Airborne Spectrographic Imager (CASI ) data. Preliminary evaluation of commonly available classification algorithms produced two candidate techniques: the Maximum Likelihood Classifier (MLC) and the Spectral Angle Mapper (SAM). Pre-classification enhancements and the two different classifiers were compared. Ten different dataset combinations were created for two pilot sites: one at Crayford Marsh and one at Dartford Creek. These consisted of the original CASI bandset (15 bands in spatial mode from blue to near-infrared) and nine other combinations resulting from band subsets, Principal Component Analysis (PCA) and Normalized Difference Vegetation Index (NDVI ). Twelve classes were established for each site although only some of these were common to both. Each classified image was accuracy assessed using a combination of field mapping, field photographs and air photograph interpretation as reference data. The most accurate classification (68% for Crayford Marsh and 53% for Dartford Creek) for both sites comprised the use of MLC with a dataset created from PCs 2, 3 and 4 from a PCA carried out on the original 15 band data, combined with an additional NDVI band. CASI data proved useful for the mapping of salt-marsh vegetation and sediments especially in the Crayford Marsh site. In the Dartford Creek site, however, there was significant confusion between some classes. Further work is recommended to test the classification