Optimization algorithms in FMRF model-based segmentation for LIDAR data and co-registered bands

Cao, Y., Wei, H. ORCID: https://orcid.org/0000-0002-9664-5748 and Zhao, H.J. (2008) Optimization algorithms in FMRF model-based segmentation for LIDAR data and co-registered bands. In: 5th IAPR Workshop on Pattern Recognition in Remote Sensing, Tampa, Florida, USA. doi: 10.1109/PRRS.2008.4783166

Abstract/Summary

In this paper, a fuzzy Markov random field (FMRF) model is used to segment land-objects into free, grass, building, and road regions by fusing remotely, sensed LIDAR data and co-registered color bands, i.e. scanned aerial color (RGB) photo and near infra-red (NIR) photo. An FMRF model is defined as a Markov random field (MRF) model in a fuzzy domain. Three optimization algorithms in the FMRF model, i.e. Lagrange multiplier (LM), iterated conditional mode (ICM), and simulated annealing (SA), are compared with respect to the computational cost and segmentation accuracy. The results have shown that the FMRF model-based ICM algorithm balances the computational cost and segmentation accuracy in land-cover segmentation from LIDAR data and co-registered bands.