By S.S. Ali, P.M. Dare, S.D. Jones (auth.), Simon Jones, Karin Reinke (eds.)
Remote sensing of the environment is changing into more and more available and demanding in today’s society. This publication goals to focus on the various large and multi-disciplinary functions, and rising practices, that distant sensing and photogrammetric applied sciences lend themselves to. The papers were chosen from the thirteenth and 14th Australasian distant Sensing and Photogrammetry meetings given via specialists in distant sensing, spatial research and photogrammetry from around the Asia Pacific area. they're provided right here as a set of peer reviewed papers masking examine into parts akin to facts fusion suggestions and their functions in environmental tracking, synoptic tracking and knowledge processing, terrestrial and marine purposes of distant sensing, and photogrammetry.
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In the data fusion process, the evaluation of the results becomes relatively complex due to the involvement of different data sources. The different aspects of image acquisition of the various sensors have to be considered as well as the approach of the image fusion itself plays a role. In this analysis, both visual methods and statistical parameters were selected as the assessment criteria. From visually inspecting the results, the main difference between pixel- and object-level fusions is the sharpness of the classified feature.
This paper compares the results of the pixel- and object-level fusion of a lidar derived DSM with colour aerial photography and multispectral imagery. The comparison is based on the assessment of the classification accuracy where reference information has been collected through field survey. Pixel-level fusion of the colour photography and the DSM exhibits better results than sole classification of colour photography. The same result is found for the multispectral imagery and the DSM. Object-level fusion achieves superior results compared to all pixel-level classification of tested categories.
F. ) Visualisation in Modern Cartography. Oxford, Elsevier. Kurtener, D. and Badenko, V. (2001) GIS Fuzzy Algorithm for Estimating the Quality of Soil Parameters: Evaluation of Attribute Data Quality. GIM International, March, Vol. 15, 76–79. Leitner, M. and Buttenfield, B. (1997) Cartographic Guidelines on the Visualization of Attribute Accuracy. Proceedings of AUTO-CARTO 13. Seattle. Leitner, M. and Buttenfield, B. P. (2000) Guidelines for the Display of Attribute Certainty. Cartography and Geographic Information Science, 27, 3–14.
Innovations in Remote Sensing and Photogrammetry by S.S. Ali, P.M. Dare, S.D. Jones (auth.), Simon Jones, Karin Reinke (eds.)