Evaluation of pansharpening algorithms in support of earth observation based rapid-mapping workflows

Abstract

In order to satisfy the humanitarian information demand in ongoing- and post-crisis situations, earth observation (EO) data must be streamed through time-critical workflows. Data fusion serves as an integral segment of EO-based rapid-mapping workflows. Fused images form the basis for manual, semi-, and fully-automated classification steps in the information retrieval chain. Many fusion algorithms have been developed and tested for different remote sensing applications, however, the efficacy of data fusion is weakly assessed in the context of rapid-mapping workflows. In this research, we investigated how different fusion algorithms perform when applied to very high spatial resolution (VHSR) satellite images that encompass ongoing- and post-crises scenes. The evaluation entailed twelve fusion algorithms: Brovey transform, color normalization spectral sharpening (CN) algorithm, Ehlers fusion algorithm, Gram-Schmidt fusion algorithm, high-pass filter (HPF) fusion algorithm, local mean matching algorithm, local mean variance matching (LMVM) algorithm, modified intensity-hue-saturation (HIS) fusion algorithm, principal component analysis (PCA) fusion algorithm, subtractive resolution merge (SRM) fusion algorithm, the University of New Brunswick (UNB) fusion algorithm, and the wavelet-PCA fusion algorithm. These algorithms were applied to GeoEye-1 satellite images taken over three geographical settings representing natural and anthropogenic crises that occurred recently: earthquake-damaged sites in Haiti, flood-impacted sites in Pakistan, and armed-conflicted areas and internally displaced persons (IDP) camps in Sri Lanka. Fused images were assessed for spectral and spatial fidelity using a variety of quantitative quality indicators and visual inspection methods. Spectral quality metrics include correlation coefficient, root-mean-square-error (RMSE), relative difference to mean, relative difference to standard deviation, spectral discrepancy, deviation index, peak-signal-to-noise ratio index, entropy, mean structural similarity index, spectral angle mapper, and relative dimensionless global error in synthesis. The spatial integrity of fused images was assessed using Canny edge correspondence, high-pass correlation coefficient, and RMSE of Sobel-filtered edge images. Under each metric, fusion algorithms were ranked and best competitors were identified. Ehlers, WV, and HPF had the best scores for the majority of spectral quality indices. UNB and Gram-Schmidt algorithms had the best scores for spatial metrics. HPF emerged as the overall best performing fusion algorithm.