14 March 2003 Knowledge-based land use classification from IKONOS imagery for Arkadi, Crete, Greece
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Abstract
The objective of the present research effort was the investigation of expert system classification techniques for land use mapping from very high resolution images for a typical Greek landscape. Data used included an IKONOS image of the Arkadi area in Crete acquired on September 2000, and a digital terrain model. Photointerpretation was carried out using color composites, band ratios and maps of scale 1:5.000 and 1:50.000. Maximum likelihood was used for per pixel supervised classification and its accuracy was 72%. A knowledge base containing 51 rules, 44 hypotheses and 12 variables was developed in the Expert Classifier module of ERDAS Imagine. A hierarchical organization of thematic classes was developed in four levels through photointerpretation and study of the spectral reflectance diagrams and thematic class histograms. The image was first classified into three general categories: water-like, vegetation-like and soil-like materials. These were then separated into sub-classes. Classification rules were enriched with ancillary data such as the slopes, the road network, the NDVI vegetation index, the results of a spatial model computing texture, and indices reflecting the polygon shape and perimeter. Overall accuracy of the classification with the expert system was 82%.
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Demetre P. Argialas, Demetre P. Argialas, Vassiliki Goudoula, Vassiliki Goudoula, } "Knowledge-based land use classification from IKONOS imagery for Arkadi, Crete, Greece", Proc. SPIE 4886, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology II, (14 March 2003); doi: 10.1117/12.463282; https://doi.org/10.1117/12.463282
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