A Performance Analysis of Quality Assessment Metrics of Satellite Fused Images through Spectral and Spatial Distortion Modeling

Document Type : علمی - پژوهشی

Authors

1 M.Sc. student, Faculty of Geography, Remote sensing and GIS department, University of Tehran, Iran

2 Assistant Prof., Faculty of Electrical, Sadra University, Iran

3 Assistant Prof., Faculty of Geography, Remote sensing and GIS department, University of Tehran, Iran

Abstract

Satellite image fusion and creating data with spectral and spatial capabilities greater than those of the existing data is of special interest and position in Remote Sensing. However, the accuracy and efficiency of all processing stages of using these data depend on the precision and reliability of the produced data. The optimum utilization of fused images relies, ultimately, on the precision of the employed fusion method. Evaluation of this important aspect requires selection of an optimum assessment metric which is appropriate for the objectives and application areas of fused images. Different application areas such as, natural resources, civil areas and etc. have different preferences with regard to maintaining the spectral and spatial data. Therefore, selection of the best fusion method, that is appropriate for the application area of the image, through image quality assessment metrics is one of the users’ challenges in this field. The present paper, thus, attempts to provide an analysis and assessment of 20 common image quality assessment methods so as to identify and introduce the most optimum metrics based on the area of application of fused images. It also tries to introduce the factors causing differences in the way quality is assessed by the metrics. And then present a model for identifying the capabilities of each metric for displaying the distortions that occur in the spectral and spatial aspects of data. To this end, two metrics of high-pass filter and spectral angle mapper are taken into consideration as spectral and spatial data comparison bases, and the performance of metrics with regard to their assessment of the quality of simulated data, that contain images with controlled spectral and spatial distortions, is evaluated. Spectral distortions were introduced by high-pass filter effect, band displacement and changing color tone. Low-pass filter and attrition filters with structural elements of different dimensions were also used for introducing spatial distortions. Due to offering different spectral and spatial resolutions, images from Landsat8, EO-1, and Worldview satellites were used. Pieces with different land applications were cropped from these images to serve as test images. The assessment of the metrics tested on these images resulted in the categorization of metrics into three groups as per their capability for displaying spectral and spatial distortions. The first group included methods that functioned on the basis of noise for overall assessment of images with respect to their noise; these methods included ERGAS, MSE, PSNR, WSNR, and SNR indices. The second group were those aligned with Spectral Angular Mapper method that are suitable for assessment of images with sensitive applications as they display the spectral distortions with greater precision; These methods include BIAS, RASE, Q, MSSIM, NQM, FSIM, SRSIM, and SAM indices. The third group is also compatible with high-pass filter of HPF, RFSIM and MAD that are of a greater capability for displaying spatial distortions.

Keywords


  1. Al-Wassai, F. A., Kalyankar, N.V. & Al-Zuky, A.A., 2011, Arithmetic and Frequency Filtering Methods of Pixel-Based Image Fusion Techniques, Computer vision and pattern recognition, arXiv preprint arXiv: 1107.3348.
  2. Chandler, D.M. & Hemami, S.S., 2007, VSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images, Image Processing, IEEE Transactions on, 16(9), 2284-2298.
  3. Damera-Venkata, N., Kite, T.D., Geisler, W.S., Evans, B.L. & Bovik, A.C., 2000, Image Quality Assessment Based on a Degradation Model, Image Processing, IEEE Transactions on, 9(4), 636-650.
  4. Han, S.S., Li, H.T. & Gu, H.Y., 2008, The Study on Image Fusion for High Spatial Resolution Remote Sensing Images, Int Arch Photogram Rem Sens Spatial Inform Sci, 37, 1159-1163.
  5. Hoult, D.I. & Richards, R.E., 1976, The Signal-to-Noise Ratio of the Nuclear Magnetic Resonance Experiment, Journal of Magnetic Resonance (1969), 24(1), 71-85.
  6. Hu, X., Lu, H., Zhang, L. & Serikawa, S., 2010, A New Type of Multi-Focus Image Fusion Method Based on Curvelet Transforms, In Electrical and Control Engineering (ICECE), 2010, June, International Conference on pp. 172-175.
  7. Kite, T.D., Evans, B.L. & Bovik, A.C., 2000, Modeling and Quality Assessment of Halftoning by Error Diffusion, Image Processing, IEEE Transactions on, 9(5), 909-922.
  8. Kim, Y., Lee, C., Han, D., Kim, Y. & Kim, Y., 2011, Improved Additive-Wavelet Image Fusion, Geoscience and Remote Sensing Letters, IEEE, 8(2), 263-267.
  9. Klonus, S. & Ehlers, M., 2009, Performance of Evaluation Methods in Image Fusion, In Information Fusion, 2009, July, FUSION'09. 12th International Conference on pp. 1409-1416 IEEE.
  10. Larson, E.C. & Chandler, D.M., 2010, Most Apparent Distortion: Full-Reference Image Quality Assessment and the Role of Strategy, Journal of Electronic Imaging, 19(1), 011006-011006.
  11. Mattappillil, J., Soundarya Mala, P., Sailaja, V. & Mahidar, R., 2013, Comparative Evaluation of Image Fusion Technique Using Shift Invariant Transforms, Journal of Engineering Research and Application,(3), 1073-1076.
  12. Raut, G.N., Paikrao, P.L. & Chaudhari, D.S., 2013, A Study of Quality Assessment Techniques for Fused Images, International Journal of Innovative Technology and Exploring Engineering (IJITEE), 2, 290-294.
  13. Robila, S.A., 2005, Using Spectral Distances for Speedup in Hyperspectral Image Processing, International Journal of Remote Sensing, 26(24), 5629-5650.
  14. Sayood, K., 2002, Statistical Evaluation of Image Quality Measures, Journal of Electronic imaging, 11(2), 206-223.
  15. Shi, W., Zhu, C., Tian, Y. & Nichol, J., 2005, Wavelet-Based Image Fusion and Quality Assessment, International Journal of Applied Earth Observation and Geoinformation, 6(3), 241-251.
  16. Wald, L., 2000, Quality of High Resolution Synthesised Images: Is There a Simple Criterion?, In Proceedings, pp. 99-103.
  17. Wang, Z. & Simoncelli, E.P., 2004, Stimulus Synthesis for Efficient Evaluation and Refinement of Perceptual Image Quality Metrics, In Electronic Imaging ,2004, June, pp. 99-108, International Society for Optics and Photonics.
  18. Wang, Z. & Bovik, A.C., 2002, A Universal Image Quality Index, Signal Processing Letters, IEEE, 9(3), 81-84.
  19. Wang, Z., Bovik, A.C., Sheikh, H.R. & Simoncelli, E.P.,2004, Image Quality Assessment: from Error Visibility to Structural Similarity, Image Processing, IEEE Transactions on, 13(4), 600-612.
  20. Yuhendra, Y., Alimuddina, I., Sri Sumantyoa, J.T. & Kuze, H., 2012, Assessment of Pan-Sharpening Methods Applied to Image Fusion of Remotely Sensed Multi-Band Data, International Journal of Applied Earth Observation and Geoinformation ,18. 165–175.
  21. Zhang, F., Ma, L., Li, S. & Ngan, K.N., 2011, Practical Image Quality Metric Applied to Image Coding, Multimedia, IEEE Transactions on 13(4), 615-624.
  22. Zhang, L. & Li, H., 2012, SR-SIM: A Fast and High Performance IQA Index Based on Spectral Residual, In Image Processing (ICIP), 2012, September, 19th IEEE International Conference on pp. 1473-1476, IEEE.
  23. Zhang, L., Zhang, D. & Mou, X., 2010, RFSIM: A Feature Based Image Quality Assessment Metric Using Riesz Transforms, In Image Processing (ICIP), 2010, September, 17th IEEE International Conference on pp. 321-324, IEEE.
  24. Zhang, L., Zhang, D. & Mou, X., 2011, FSIM: A Feature Dimilarity Index for Image Quality Assessment, Image Processing, IEEE Transactions on, 20(8), 2378-2386.
  25. Zhang, Yun., 2008, Methods for Image Fusion Quality Assessment-A Review, Comparison and Analysis, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37, 1101-1109.