Research Papers

Improved method for discriminating agricultural crops using geostatistics and remote sensing

[+] Author Affiliations
Costanza Fiorentino

University of Basilicata, Department Crop Systems, Forestry and Environmental Sciences, Potenza, 85100 Italy

Cristina Tarantino, Guido Pasquariello

National Research Council, Institute of Intelligent Systems for Automation, Bari, 70125 Italy

Bruno Basso

University of Basilicata, Department Crop Systems, Forestry and Environmental Sciences, Potenza, 85100 Italy

Michigan State University, W. K. Kellogg Biological Station, 3700 East Gull Lake Drive, Hickory Corners, MI 49060

Queensland University of Technology, Institute for Sustainable Resources, Brisbane, 2 George St, GPO Box 2434, Brisbane QLD 4001, Australia

J. Appl. Remote Sens. 5(1), 053536 (July 12, 2011). doi:10.1117/1.3601437
History: Received January 13, 2011; Revised April 23, 2011; Accepted May 27, 2011; Published July 12, 2011; Online July 12, 2011
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Reliable land cover mapping of agricultural areas require high resolution remote sensing and robust classification techniques. In this paper, we propose the integration of spectral information with spatial information using the traditional statistical supervised classifier “Maximum Likelihood” and a geostatistical tool, “Indicator Kriging” algorithm, for the development of land cover maps by supervised classification from remotely sensed data at medium and high spatial resolution. The proposed method showed better results in classes’ discrimination with smoother resulting maps than the ones produced using only spectral information. Two different satellites imagery were analyzed: a Landsat TM5 image at medium spatial resolution acquired during 2006 and an Ikonos II image at higher spatial resolution acquired during 2008. The better performance of the “combined” approach compared to the traditional Maximum Likelihood technique was confirmed by confusion matrix. The overall accuracy increases from 76.16% to 85.96% for LandsatTM image and from 71.56% to 80.25% for the IKONOS image.

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© 2011 Society of Photo-Optical Instrumentation Engineers (SPIE)

Citation

Costanza Fiorentino ; Cristina Tarantino ; Guido Pasquariello and Bruno Basso
"Improved method for discriminating agricultural crops using geostatistics and remote sensing", J. Appl. Remote Sens. 5(1), 053536 (July 12, 2011). ; http://dx.doi.org/10.1117/1.3601437


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