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Rule-based improvement of maximum likelihood classified LIDAR data fused with co-registered bands

In the past decade, LIght Detection And Ranging (LIDAR) has been recognised by both the commercial and public sector as a reliable and accurate source for land surveying. Object classification in LIDAR data tends towards data fusion by employing additional simultaneously recorded bands. In this paper, a rule-based approach is presented for improving classification accuracy obtained in a supervised classification based on Maximum Likelihood using high resolution LIDAR data, co-registered bands, and further height derived texture features. The issues regarding feature and class selection as well as differentiated accuracy assessment are addressed. The results show that incorporating additional knowledge and considering relationship among classes is beneficial for improving classification accuracy in fused LIDAR data sets.