Application of Taguchi method to improve land use land cover classification using PCA-DWT-based SAR-multispectral image fusion

The fusion of multispectral and synthetic aperture radar (SAR) images is of vital importance in many remote sensing applications. Spectral distortion and trade-off between the spatial and spectral quality of the fused image are significant issues in SAR-multispectral image fusion. Our study attempts to improve the performance of SAR-multispectral image fusion concerning these two issues. The primary objective of our study is to optimize the performance of hybrid fusion approach based on principal component analysis and discrete wavelet transform (PCA-DWT) using Taguchi orthogonal array. The fused data are evaluated using visual analysis and standard quality metrics. The results are compared with recent hybrid fusion approaches applied to the SAR-multispectral image fusion. The utility of the fused data is evaluated based on the remote sensing application, namely, land use land cover classification. The classification results are compared to a standard thematic map available on the Bhuvan geoportal to check classification accuracy. A comparative analysis with recent hybrid approaches conclusively demonstrates that the proposed optimization in the PCA-DWT based fusion is superior to conventional hybrid methods. (C) 2021 Society of Photo-Optical Instrumentation Engineers (SPIE)

Automated summary

The study aims to optimize the performance of SAR-multispectral image fusion using PCA-DWT method, evaluate the quality of the fused data, and examine the utility of fused data in land use classification. The proposed optimization in the PCA-DWT based fusion is demonstrated to be superior to conventional hybrid methods through comparative analysis.