Comparison and Fusion of Multispectral and Panchromatic IKONOS Images Using Different Algorithms

The fusion of multi-resolution remote sensing images has become a hot issue for enhancing the original images. In comparison with low- and middle-resolution remote sensing imagery, high spatial resolution images have competitive advantages in identifying fine spatial features of land cover features. In this study, an IKONOS image of Hefei, Anhui Province, was used to compare the fusion effects based on seven typical transform methods including the HSV (Hue-Saturation-Value), Brovey, Wavelet Transform (WT), Principal Component (PC) transform, Gram-Schmidt, Pan Sharpening, and Color Normalized (CN) transform. The spatial texture, spectral feature and classification accuracy were used to evaluate the fusion effects. The results showed that PC transform had a optimal performance; WT transform had better ability to keep spectral information; Pan Sharpening provided superior structure information and classification effect; Gramm-Schmidt transform had better spatial and spectral information but a general classification effect; CN transform maintained a good spectral information; and Brovey transform was the worst algorithm. In addition, the classification was also performed using the seven fused images and the accuracy was 85.03 %, 84.20 %, 90.26 %, 88.18 %, 85.48 %, 88.18 %, 85.48 %, respectively.