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 info rmation since data is collected using significantly different frequenc ies, polarizations, and look angles (ERS-1: C-band, VV polarization, 2 3 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 eit her of the two sensors alone. A conceptual model is presented to show how simple structural attributes of terrain surfaces and vegetation co ver relate to the data from these two sensors, The conceptual model is knowledge-based; and it is supported by both theoretical consideratio ns and experimental observations, The knowledge-based, conceptual mode l is incorporated into a classifier that uses hierarchical decision ru les to differentiate land-cover classes, The land-cover classes are de fined on the basis of generalized structural properties of widespread applicability, The classifier operates sequentially and produces two l evels of classification, At Level-1, terrain is structurally different iated into man-made features (urban), surfaces, short vegetation, and tall vegetation, At Level-2, the tall vegetation class is differentiat ed on the basis of plant architectural properties of the woody stems a nd 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 comp osite classifier yields overall accuracies in excess of 90% for a test site in northern Michigan located along the southern ecotone of the b oreal forest, For the area examined, the SAR-based classification is s uperior to unsupervised classification of multitemporal AVHRR data sup plemented with a priori information on elevation, climate, and ecoregi on.