Abstract:
This research presents a novel strategy for hyperspectral and LiDAR data fusion to generate accurate LC/LU maps with object based classification method. Unlike convention...Show MoreMetadata
Abstract:
This research presents a novel strategy for hyperspectral and LiDAR data fusion to generate accurate LC/LU maps with object based classification method. Unlike conventional object-based strategies (hierarchical and multilevel models), in the proposed method, classification has been performed in an iterative Segmentation-Classification-Merging (SCM) process. In each iteration, image objects are extracted by using their spectral, height, geometric and class-related characteristics based on data availability, class importance and higher extraction capability. Results indicate great overall accuracy of 97.33% and a kappa coefficient of 0.9710.
Published in: 2014 IEEE Geoscience and Remote Sensing Symposium
Date of Conference: 13-18 July 2014
Date Added to IEEE Xplore: 06 November 2014
Electronic ISBN:978-1-4799-5775-0
ISSN Information:
Department of Remote Sensing Engineering, Graduate University of Advanced Technology, Kerman, Iran
Department of Civil Engineering, Iran University of Science and Technology, Tehran, Iran
Department of Remote Sensing Engineering, Graduate University of Advanced Technology, Kerman, Iran
Centre for Cold Ocean Resources Engineering (C-CORE), LOOKNorth Program, St. John's, NL, CANADA
Contents Department of Remote Sensing Engineering, Graduate University of Advanced Technology, Kerman, Iran
Department of Civil Engineering, Iran University of Science and Technology, Tehran, Iran
Department of Remote Sensing Engineering, Graduate University of Advanced Technology, Kerman, Iran
Centre for Cold Ocean Resources Engineering (C-CORE), LOOKNorth Program, St. John's, NL, CANADA
