Evaluating and Improving the Functionality of Simulated Annealing Algorithm for Sub-pixel Land Cover Mapping Using Multispectral Imagery

Document Type : علمی - پژوهشی

Authors

1 MSc Student, Department of Remote Sensing Engineering, K. N. Toosi University of Technology

2 Associated Professor, Department of Remote Sensing Engineering, K. N. Toosi University of Technology

3 Assistant Professor, Department of Remote Sensing Engineering, K. N. Toosi University of Technology

Abstract

The mixed pixels are considered as a major challenge in land cover mapping procedure from satellite imagery. Developments of the spectral unmixing and soft classification methods have provided the possibility for estimation of class proportions within the pixels. However, sub-pixel land cover mapping requires the spatial allocation of the sub-pixels. Recently, the Super Resolution Mapping (SRM) techniques have been developed for optimization of the sub-pixels spatial arrangement using the outputs of soft classifiers and based on the concepts of spatial dependency. In this research, the overall capability of the simulated annealing algorithm was evaluated through sub-pixel land cover mapping of the study area. To do so, a novel method was proposed for generating new solutions in each step of the algorithm and then the results were compared to the traditional method. On the other hand, the effective parameters on the performance of the algorithm (e.g. zoom factor, cooling function type, static and dynamic iterations) were investigated. According to the obtained results, higher values of zoom factor yields more promising overall accuracy . Also, the geometric function was found as the optimal cooling function with respect to the overall accuracy and processing speed. Meanwhile, dynamic iterations demonstrated more accuracy than the static case. As another key result of the paper, the proposed method for generating the new solutions in simulated annealing algorithm is led to increasing of the overall accuracy and also reducing the processing time of algorithm up to 50 percent. The most accurate result of the proposed algorithm, which was obtained for the that case of being independent from soft classifier, is determined 94.97 percent

Keywords


  1. Atkinson, P.M., 1997, Mapping Sub-Pixel Boundaries from Remotely Sensed Imagesو In: Z. Kemp (Ed.), Innovations in GIS 4, 166–180.
  2. Atkinson, P.M., 2009, Issues of Uncertainty in Super-Resolution Mapping and Their Implications for the Design of an Inter-Comparison Study, International Journal of Remote Sensing, 30, (20), 5293-5308.
  3. Atkinson, P.M., 2005, Super-Resolution Target Mapping from Soft Classified Remotely Sensed Imagery, Photogrammetric Engineering and Remote Sensing, 71, (7), 839-846.
  4. Brown de Colstoun, E.C., Story, M.H., Thompson, C., Commisso, K., Smith, T.G. & Irons, J.R., 2003, National Park Vegetation Mapping Uusing Multitemporal Landsat 7 Data and a Decision Tree Classifier, Remote Sensing of Environment, 85:316–327.
  5. Cerny, V., 1985, A Thermodynamical Approach to the Traveling Salesman Problem: an Efï‌cient Simulation Algorithm, Journal of Optimization Theory and Applications, 45:41–51.
  6. Cracknell, A.P., 1998. Synergy in Remote Sensing - What’s in a Pixel?, International Journal of Remote Sensing, 19 (11), 2025– 2047.
  7. Dekkers A. & Aarts, E., 1991. Global Optimization and Simulated Annealing, Mathematical Programming, 50:367–393.
  8. Fisher, P., 1997, The Pixel: A Snare and a Delusion, International Journal of Remote Sensing, 18, pp. 679–685.
  9. Foody, G.M., 1996, Approaches for the Production and Evaluation of Fuzzy Land Cover Classifications from Remotely Sensed Data, International Journal of Remote Sensing, 17, 1317-1340.
  10. Foody, G.M. & Cox, D.P., 1994. Sub-Pixel Land Cover Composition Estimation Using a Linear Mixture Model and Fuzzy Membership Functions, International Journal of Remote Sensing 15: pp. 619-631.
  11. Friedl, M.A., McIver, D.K., Hodges, J.C.F., Zhang, X.Y., Muchoney, D. & Strahler, A.H., 2002, Global Land Cover Mapping from MODIS: Algorithms and Early Results, Remote Sensing of Environment, 83,287−302.
  12. Garcia-Haro, F.J., Gilabert, M.A. & Melia, J., 1996, Linear Spectral Mixture Modeling To Estimate Vegetation Amount from Optical Spectral Data, International Journal of Remote Sensing, 17: pp. 3373-3400.
  13. Geman, S. & Geman, D., 1984, Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:721–741.
  14. Gonzalez-Audicana, M., Saleta, J.L., Garcia Catalan, R. & Garcia, R., 2004, Fusion of Multispectral and Panchromatic Images Using Improved IHS and PCA Mergers Based on Wavelet Decomposition, IEEE Transactions On Geoscience And Remote Sensing, 42 (6), 1291- 1299.
  15. Ingber, L., 1996, Adaptive Simulated Annealing, Control and Cybernetics, 25(1):33–54.
  16. Kanellopoulos, I., Varfis, A., Wilkinson, G.G. & Megier, J., 1992. Land Cover Discrimination in SPOT HRV Imagery Using an Artificial Neural Network: A 20 Class Experiment, International Journal of Remote Sensing, 13 (5), 917–924.
  17. Kasetkasem, T., Arora, M. K. & Varshney, P. K., 2005, Super-Resolution Land Cover Mapping Using a Markov Random Field Based Approach, Remote Sensing of Environment, 96(3-4), 302-314.
  18. Kirkpatrick, S., Gelatt, C.D. & Vecchi, M.P., 1983, Optimization by Simulated Annealing, Science, 220(4598):671–680.
  19. Locatelli, M., 2000, Simulated Annealing Algorithms for Continuous Global Optimization: Convergence Conditions, Journal of Optimization Theory and Applications, 29(1):87–102.
  20. Mertens, K.C., Verbeke, L.P.C. & Ducheyne, EI. De Wulf., RR., 2003, Using Genetic Algorithms in Sub-Pixel Mapping, International Journal of Remote Sensing, 24: 4241–4247
  21. Ozdamar, L. & Demirhan, M., 2000, Experiments with New Stochastic Global Optimization Search Techniques, Computers and Operations Research, 27(9):841–865.
  22. Paola, J.D. & Schowengerdt, R.D., 1995, Review Article: A Review and Analysis of Back Propagation Neural Networks for Classiï‌cation of Remotely Sensed Multispectral Imagery, International Journal of Remote Sensing, 16: pp. 3033-3058.
  23. Saab, Y. & Rao, V., 1991, Combinational Optimization by Stochastic Evolution, IEEE Transactions on Computer-Aided Design, 10:525–535.
  24. Talbi, E.-G., 2009, Metaheuristics : From Design to Implementation, New Jersey: John Wiley & Sons.
  25. Tatem A.J, Lewis, H.G., Atkinson, P.M. & Nixon, M.S., 2003, Super Resolution Land Cover Mapping from Remotely Sensed Imagery Uusing a Hopfield Neural Network, in Uncertainty in Remote Sensing and GIS, Hoboken, NJ: Wiley, 77–98
  26. Thornton, M.W., Atkinson, P.M. & Holland, D.A., 2006, Super-Resolution Mapping of Rural Land Cover Features from Fine Spatial Resolution Satellite Sensor Imagery, International Journal of Remote Sensing, 27: 473–491
  27. Tobler, W., 1970, A Computer Movie Simulating Urban Growth in the Detroit Region, Economic Geography, 46(2): 234-240.
  28. Villa, A., Chanussot, J., Benediktsson, J.A. & Jutten, Ch., 2011, Spectral Unmixing for the Classiï‌cation of Hyperspectral Images at a Finer Spatial Resolution, IEEE Journal of Selected Topics in Signal Processing, Vol. 5, No. 3. 521-533.