A Multi-resolution Analysis and Classification Framework for Improving Land Use/cover Mapping from Earth Observation Data

The last few years have seen satellite platforms with a large number of sensors (e.g. Terra and ENVISAT) coming on-line and the launching of a huge number of satellites with more than one sensor (e.g. IKONOS and QuickBird). Various satellite images with spatial resolutions ranging from 0.5 to 25,000 m are available for different applications. This development offers new and significant changes and challenges in the approach to analysis, integration, and the efficient spatial modelling of these observation data. This paper presents a multi-resolution analysis and classification framework for selecting and integrating suitable information from different spatial resolutions and analytical techniques into classification routines. The proposed framework focuses on the examination of image structural using different spatial analytical techniques in order to select appropriate methods in different stages of classification such as training strategy, feature extraction, scene models, and classification accuracy assessment. The multiresolution approaches are tested using simulated multi-resolution images from IKNOS data for a portion of western part of the Kingston Metropolitan area. It was demonstrated that the multi-resolution classification approaches can significantly improve land use/cover classification accuracy when compared with those from single-resolution approaches.