Research Cell: An International Journal of Engineering Sciences

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Assessment of Image Classifications Using Compressed Multispectral Satellite Data (MSD)


Affiliations
1 Department of Computer Science, Gurukula Kangri Vishwavidyalaya, Haridwar (UK), India
2 Department of Civil Engineering, Indian Institute of Technology, Roorkee (UK), India
 

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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  • Assessment of Image Classifications Using Compressed Multispectral Satellite Data (MSD)

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Authors

Shwetank
Department of Computer Science, Gurukula Kangri Vishwavidyalaya, Haridwar (UK), India
Karamjit Bhatia
Department of Computer Science, Gurukula Kangri Vishwavidyalaya, Haridwar (UK), India
Kamal Jain
Department of Civil Engineering, Indian Institute of Technology, Roorkee (UK), India

Abstract


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.