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Preprocesing of Hyperspectral Images for Precision Agriculture


Hemalata V. Bhujle , SDMCET DHARWAD


Hyperspectral Images, Precision Agriculture


Precision agriculture requires high spectral and spatial resolution imagery for advanced analyses of crop and soil conditions to increase crop yield. Imagery from traditional satellite systems, such as the U.S. Landsat satellites and the French SPOT satellites, has long been used to monitor crop growing conditions and to estimate crop yields over large geographic areas. Hyperspectral images are the images captured in large continuous narrow wavebands provides significant advancement in understanding the subtle changes in biochemical and biophysical attributes of the crop plants and their different physiological processes. Hyperspectral images captured in various narrow bands individually contain complementary information. Fusion of hyperspectral images using hierarchical model is proposed in the work which provides a single image containing all complementary information in a single image. Hyperspectral images have been preprocessed for atmospheric correction, radiometric correction and noise removal. Noise removal is carried out in multiresolution framework by applying Bayes thresholding at detail coefficients. Approximate coefficients are also filtered using NLM filter.

Other Details

Paper ID: IJSRDV6I120053
Published in: Volume : 6, Issue : 12
Publication Date: 01/03/2019
Page(s): 88-90

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