Abstract
Texture analysis has been efficiently utilized in the area of terrain classification. The widely used co-occurrence features have been reported most effective for this application. Since the number of co-occurrence features is very high, a terrain classifier based on co-occurrence features should deal with high dimensionality problem. This paper deals with how to solve high dimensionality problems by employing a conventional linear discriminant classifier and clustering algorithms based on ANN (Artificial Neural Network). A implemented linear discriminant classifier is based on dimensionality reduction by using FST (Foley-Sammon transform), and its result is compared with ANN clustering algorithm FCM (Fuzzy C-mean). Experimental results show that the overall classification accuracy using clustering algorithm is good, especially for some particular classes.
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.
