An improved method to discriminating agricultural crops using geostatistics and remote sensing

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.