ARS  Vol.4 No.4 , December 2015
Evaluation of EO-1 Hyperion Data for Crop Studies in Part of Indo-Gangatic Plains: A Case Study of Meerut District
Abstract: Due to the high number of bands in the hyperspectral image, the selection of optimum bands for crop classification is a prerequisite. The Hyperion sensor has 242 spectral bands out of which 143 useable bands were selected. The bands reflected wavelength from 400 to 1000 nm to the VNIR spectrometer and transmitted the band from 900 to 2500 nm to the SWIR spectrometer. Spectral Angle Mapping Classification (SAMC) approach and a multi-scale object oriented method are applied for crop studies. The result obtained from the accuracy assessment by comparing Ground Control Points (GCP) with the help of image spectra shows 78% of overall accuracy. This shows that these data are highly useful in studying the crop diversification.
Cite this paper: Singh, D. and Singh, R. (2015) Evaluation of EO-1 Hyperion Data for Crop Studies in Part of Indo-Gangatic Plains: A Case Study of Meerut District. Advances in Remote Sensing, 4, 263-269. doi: 10.4236/ars.2015.44021.

[1]   Jensen, J.R. (2005) Introductory Digital Image Processing. 3rd Edition, Prentice Hall, Upper Saddle River.

[2]   Broge, N.H. and Leblanc, E. (2000) Comparing Prediction Power and Stability of Broadband and Hyperspectral Vegetation Indices for Estimation of Green Leaf Area Index and Canopy Chlorophyll Density. Remote Sensing of Environment, 76, 156-172.

[3]   Philipp, I. and Rath, T. (2002) Improving Plant Discrimination in Image Processing by Use of Different Colour Space Transformations. Computers and Electronics in Agriculture, 35, 1-15.

[4]   Galvao, L.S., Formaggio, A.R. and Tisot, D.A. (2005) Discrimination of Sugarcane Varieties in Southeastern Brazil with EO-1 Hyperion Data. Remote Sensing of Environment, 95, 523-534.

[5]   Goodenough, D.G., Dyk, A., Niemann, K.O., Pearlman, J.S., Chen, H., Han, T., Murdoch, M. and West, C. (2003) Processing Hyperion and ALI for Forest Classification. IEEE Transactions on Geoscience and Remote Sensing, 41, 1321-1331.

[6]   Zarco-Tejada, P.J., Ustin, S.L. and Whiting, M.L. (2005) Temporal and Spatial Relationship between within Field Yield Variability in Cotton and High Spatial Hyperspectral Remote Sensing Imagery. Agronomy Journal, 97, 641-652.

[7]   Janssen, L.L.F. and vander Wel, F.J.M. (1994) Accuracy Assessment of Satellite Derived Land-Cover Data: A Review. Photogrammetric Engineering and Remote Sensing, 60, 419-426.