Spectral Quality Index Research Articles

Human visual system consistent quality assessment for remote sensing image fusion

Quality assessment for image fusion is essential for remote sensing application. Generally used indices require a high spatial resolution multispectral (MS) image for reference, which is not always readily available. Meanwhile, the fusion quality assessments using these indices may not be consistent with the Human Visual System (HVS). As an attempt to overcome this requirement and inconsistency, this paper proposes an HVS-consistent image fusion quality assessment index at the highest resolution without a reference MS image using Gaussian Scale Space (GSS) technology that could simulate the HVS. The spatial details and spectral information of original and fused images are first separated in GSS, and the qualities are evaluated using the proposed spatial and spectral quality index respectively. The overall quality is determined without a reference MS image by a combination of the proposed two indices. Experimental results on various remote sensing images indicate that the proposed index is more consistent with HVS evaluation compared with other widely used indices that may or may not require reference images.

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

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.

Statistical control of the spectral quality index in electron beams

Background and purposeIn radiotherapy with electron linear accelerators, it is mandatory to guarantee the stability of beam output spectra. In this work we study the quality control of the magnitude that provides the beam spectral quality, by following statistical techniques usual in industry process. MethodsWe establish the differences between the measurement of the reference quality index, R50,ion, which involves usually a costly procedure, and that of the index used for the statistical control, which can be done in an easier way and with a higher frequency. The methodology developed is applied to a Siemens Mevatron KDS electron linear accelerator, specifically for the 6, 12 and 18MeV modes. The uncertainties of both procedures are evaluated and are correlated. A retrospective analysis of the data registered during more than 13years is carried out using CUSUM algorithmic and moving range charts. ResultsThe larger uncertainties in R50,ion come from the measurement device and the observer. The uncertainty in the measurement of the statistical control variable, which is mainly due to the geometry setup, is negligible against its intrinsic variability. The application of CUSUM charts pointed out different out of control situations linked in much cases to assignable causes. In addition, moving range charts showed up situations of anomalous functioning of the LINAC. ConclusionsAlgorithmic CUSUM charts permit a nice monitoring of R50,ion. If the variability is not controlled, by using charts such as the moving range ones, the number of false alarms could increase without a change in the mean value of R50,ion.