Abstract
We propose a spatial contextual classification system for remote sensing images. In the system the observed multispectral images are modeled with a multivariate Gaussian Markov Random Field (GMRF) model and the hidden classified image is modeled with another type of MRF model. The classification is carried out from the viewpoint of Maximum a Posteriori (MAP) estimation. One of the well-known problems of MAP estimation is its high computational complexity. One way to avoid this problem is a pixelwise classification that is successfully implemented on a computer with a clique-type block matrix notation of a multivariate GMRF local conditional density function (LCDF). The proposed system is applied to real remote sensing data.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
