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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.
http://dx.doi.org/10.1109/TGRS.2003.813214

has been cited by the following article:

• TITLE: Evaluation of EO-1 Hyperion Data for Crop Studies in Part of Indo-Gangatic Plains: A Case Study of Meerut District

  AUTHORS: Davinder Singh, Rajender Singh

  KEYWORDS: Hyperspectal Remote Sensing, Hyperion Data, Spectral Angle Mapping Classification

  JOURNAL NAME: Advances in Remote Sensing, Vol.4 No.4, November 30, 2015

  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.