ABSTRACT
Fusion of multi-sensor imaging data enables a synergetic interpretation of complementary information obtained by sensors of different spectral ranges. Multi-sensor data of diverse spectral, spatial and temporal resolutions require advanced numerical techniques for analysis and interpretation. This paper reviews ten advanced pixel based image fusion techniques – Component substitution (COS), Local mean and variance matching, Modified IHS (Intensity Hue Saturation), Fast Fourier Transformed-enhanced IHS, Laplacian Pyramid, Local regression, Smoothing filter (SF), Sparkle, SVHC and Synthetic Variable Ratio. The above techniques were tested on IKONOS data (Panchromatic band at 1 m spatial resolution and Multispectral 4 bands at 4 m spatial resolution). Evaluation of the fused results through various accuracy measures, revealed that SF and COS methods produce images closest to corresponding multi-sensor would observe at the highest resolution level (1 m).
Keywords: image fusion, multi-sensor; multi-spectral, IKONOS
NOMENCLATURE
PAN |
Panchromatic |
MS |
Multi-spectral |
HSR |
High spatial resolution |
LSR |
Low spatial resolution |
COS |
Component Substitution |
LMVM |
Local Mean and Variance Matching |
IHS |
Intensity Hue Saturation |
FFT |
Fast Fourier Transform |
LP |
Low Pass |
HP |
High Pass |
SF |
Smoothing Filter |
GLP |
Generalised Laplacian Pyramid |
LR |
Local Regression |
SVHC |
Simulateur de la Vision Humaine des Couleurs |
SVR |
Synthetic Variable Ratio |
CC |
Correlation Coefficient |
UIQI |
Universal Image Quality Index |
R-G-B |
Red-Green-Blue |
NIR |
Near Infra Red |
FCC |
False colour composite |
BT |
Brovey Transform |
HPF |
High Pass Filtering |
HPM |
High Pass Modulation |
PCA |
Principal Component Analysis |
ATW |
À Trous Algorithm-Based Wavelet Transform |
MRAIM |
Multiresolution Analysis-Based Intensity Modulation |
GS |
Gram Schmidt |
LMM |
Local Mean Matching |
IRS |
Indian Remote Sensing Satellite |
MRA |
Modulation and Multi-resolution Analysis |
UNB |
University of New Brunswick |