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Assessment of Image Classifications Using Compressed Multispectral Satellite Data (MSD)
In the present study Satellite Image Processing (SIP) technique is applied on ASTER (Advance Spaceborne Thermal Emission and Reflection Radiometer) satellite image. A comprehensive spectral library of rice crop varieties: Hybrid-6129 (IET 18815), Pant Dhan-19 (IET 17544), Pusa Basmati-1 (IET-18990) and Pant Dhan-18 (IET-17920) has been developed with Blue (0.56 nm), Red (0.66 nm) and NIR (0.81 nm) spectral bands. The conventional ASTER image is classified using ML (Maximum Likelihood) classifier. The PCA (Principal Component Analysis) transformation is also applied for feature extraction to select an optimum subset of data in term of classification accuracy. Four PCs (Principal Components) images selected for PCA classification. The conventional spectral classification accuracy for rice mapping is 79.5%, which is improved up to 84.5% with PCA classification.
Keywords
SIP, PCA and ML.
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