Knowledge-based land-cover classification using ERS-1/JERS-1 SAR composites

Land-cover classification of an ERS-1/JERS-1 composite is explored in the context of regional- to global-scale applicability, Each of these orbiting synthetic aperture radars provide somewhat complementary information since data is collected using significantly different frequencies, polarizations, and look angles (ERS-1: C-band, VV polarization, 23 degrees; JERS-1: L-band, HH polarization, 35 degrees), This results in a classification procedure for the composite image (a co-registered pair from the same season) that is superior to that obtained from either of the two sensors alone. A conceptual model is presented to show how simple structural attributes of terrain surfaces and vegetation cover relate to the data from these two sensors, The conceptual model is knowledge-based; and it is supported by both theoretical considerations and experimental observations, The knowledge-based, conceptual model is incorporated into a classifier that uses hierarchical decision rules to differentiate land-cover classes, The land-cover classes are defined on the basis of generalized structural properties of widespread applicability, The classifier operates sequentially and produces two levels of classification, At Level-1, terrain is structurally differentiated into man-made features (urban), surfaces, short vegetation, and tall vegetation, At Level-2, the tall vegetation class is differentiated on the basis of plant architectural properties of the woody stems and foliage, Growth forms of woody stems include excurrent (i.e., pines), decurrent (i.e., oaks), and columnar (i.e., palm) architecture, Two classes of leaves are considered: broadleaf and needle-leaf, The composite classifier yields overall accuracies in excess of 90% for a test site in northern Michigan located along the southern ecotone of the boreal forest, For the area examined, the SAR-based classification is superior to unsupervised classification of multitemporal AVHRR data supplemented with a priori information on elevation, climate, and ecoregion.