Shape analysis and multispectral classification in geological remote sensing

This paper discusses the usage of mathematical morphology in image processing of remotely-sensed data for geologic interpretation. Particular attention is given to noise-reducing transformations of spectral bands before and after different methods of classification, and to the usage of textural context. The development of a viable processing strategy requires a multidisciplinary approach and expert knowledge in different areas: (a) geology, geomorphology, and vegetation in a study area, (b) properties of the sensor for imagery photointerpretation, (c) spectral/spatial properties of the digital data within an integrated dataset (remote sensing and ancillary data), and (d) data-processing tools including mathematical morphology theory. Examples of geometric characterization of Canadian LANDSAT scenes are described in which shape measurements are obtained using a PC-based hybrid image-processing and geographic information system, termed ILWIS, which was developed at ITC, in the Netherlands. Classes from supervised and unsupervised classification are compared to guide in geological mapping. Classes over individual occurrences of broad vegetation-landform units are studied to aid in environmental mapping. Field knowledge is the context necessary to construct expert procedures to drive sequences of data-processing steps toward a target result such as optimal classification, enhancement, or feature extraction. The interaction between expert rules and the image-processing steps can be based on synthetic measurements of shape to quantize the information either spatially or spectrally. Many useful geometrical transformations of spatially-distributed data are extensions or generalizations of spatial analysis functions typical of geographic information systems.