Simulation of Low-Resolution Panchromatic Images by Multivariate Linear Regression for Pan-Sharpening IKONOS Imageries

The extraction of spatial details is crucial for fusion quality. An efficient way is to exploit the difference between high-resolution panchromatic (Pan) images and low-resolution Pan (LRP), which is to be simulated by weighted average value from low-resolution multispectral images. To obtain the weighting coefficients with multivariate linear regression, three issues were discussed, and corresponding solutions were proposed in this letter. The proposed method consists of separating high-frequency pixels from low-frequency pixels using support vector machine and selecting observations that are evenly distributed by a bucketing technique and forcing coefficients to be sound physically by constrained least squares. Validation experiments are undertaken using three IKONOS data sets, and fusion results are compared against four popular methods. The results show that the proposed method can simulate LRP soundly and therefore achieve a better fusion quality.