A patch‐based image classification by integrating hyperspectral data with GIS
Abstract:Hyperspectral remote sensing data provide detailed spectral information and are widely used for pixel‐based image classification. However, without considering spatial correlation among neighbouring pixels, a generated thematic map may have a ‘salt‐and‐pepper' appearance. With the development of the Geographic Information System (GIS), the spatial relationship between a pixel and its neighbours can be recorded readily and used together with remote sensing data. The objective of this study was to integrate hyperspectral data with the GIS for effective thematic mapping. To date, GIS data have been used mainly in field surveys or training field selection for remote sensing data interpretation. Here we propose a patch‐classification based on integration of the GIS with remote sensing data. The classification results obtained by using this method can be easily saved in a vector format as used for GIS files. Computational cost is decreased compared with a pixel‐by‐pixel classification. The issue of how to identify pure or mixed patches is addressed and a three‐level simple and effective checking method is developed. A case study is presented with a hyperspectral data set recorded by the Pushbroom Hyperspectral Imager (PHI) and related GIS data.
Document Type: Research Article
Affiliations: 1: State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing Applications, Chinese Academy of Sciences, Datun Rd. No. 3, PO Box 9718, Beijing 100101, P. R. China 2: School of Information Technology and Electrical Engineering, University College, The University of New South Wales, Australia
Publication date: 2006-08-10