Abstract
Remote sensing image analysis has been a topic of ongoing research for many years and has led to paradigm shifts in the areas of resource management and global biophysical monitoring. Due to distortions caused by variations in signal/image capture and environmental changes, there is not a definite model for image processing tasks in remote sensing and such tasks are traditionally approached on a case-by-case basis. Intelligent control, however, can streamline some of the case-by-case scenarios and allow for faster, more accurate image processing to aid in more accurate remote sensing image analysis. This chapter will provide an evolutionary control system via two Darwinian particle swarm optimizations—one a novel application of DPSO—coupled with remote sensing image processing to help in the analysis of image data.
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Papert, S.: The Summer Vision Project. Artificial Intelligence Group, Cambridge, MA (1966)
Wagemans, J., Elder, J.H., Kubovy, M., Palmer, E.S., Peterson, M.A., Singh, M., von der Heydt, R.: A century of Gestalt psychology in visual perception: I. Perceptual grouping and figure-ground organization. Psychol. Bull. 138(6), 1172 (2012)
Schowengerdt, R.A.: Remote Sensing: Models and Methods for Image Processing, 3rd edn, pp. 13–15. Elsevier, Burlington, MA (2007)
Dey, V., Zhang, Y., Zhong, M.: A review on image segmentation techniques with remote sensing perspectives. In: Wagner, W., Szekely, B. (eds.) International Society for Photogrammmetry and Remote Sensing, XXXVIII (2010)
Yuyu, L., Zhang, M., Browne, W.N.: Image segmentation: a survey of methods based on evolutionary computation. In: Simulated Evolution and Learning, pp. 847−859. Springer International Publishing (2014)
Tuia, D., Volpi, M., Copa, L., Kanevski, M.F., Munoz-Mari, J.: A survey of active learning algorithms for supervised remote sensing image classification. IEEE J. Sel. Top. Sign. Proces. 5(3), 606–617 (2011)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Paper presented at the IEEE International Conference on Neural Networks, Perth, WA (1995)
Tillet, J., Rao, T., Sahin, F., Rao, R.: Darwinian particle swarm optimization. In: Proceedings of the 2nd Indian International Conference on Artificial Intelligence (2005). http://scholarworks.rit.edu/other/574, Accessed 3 Mar 2014
Panda, S., Padhy, N.P.: Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design. Appl. Soft Comput. 8(4), 1418–1427 (2008)
Bhamisi, P., Couceiro, M.S, Ferreira, N.M., Kumar, L.: Use of Darwinian particle swarm optimization technique for the segmentation of remote sensing images. In: 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp. 4295–4298 (2012)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. B. Cybern. 9(1), 62–66 (1979)
Aujol, J.F., Gilboa, G., Chan, T., Osher, S.: Structure-texture image decomposition—modeling, algorithms, and parameter selection. Int. J. Comput. Vision 67(1), 111–136 (2006)
Vese, L.A., Osher, S.J.: Modeling textures with total variation minimization and oscillating patterns in image processing. J. Sci. Comput. 19(1−3), 553–572 (2003)
Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60(1), 259–268 (1992)
Meyer, Y.: Oscillating patterns in image processing and nonlinear evolution equations. In: The Fifteenth Dean Jacqueline B. Lewis Memorial Lectures, vol. 22, no.1. American Mathematical Society (2001)
Xu, L., Yan, Q., Yang, X., Jia, J.: Structure extraction from texture via relative total variation. ACM Trans. Graph. 31(6), 139 (2012)
Malladi, R.J.A., Sethian, J.A., Vemuri. B.C.: Shape modeling with front propagation: a level set approach. IEEE Trans. Pattern Anal. Mach. Intell. 17(2), 158–175 (1995)
Chan, T.F., Vese, L.A.: Active contours without edges. Image Proc. IEEE Trans. 10(2), 266–277 (2001)
Vese, L.A., Chan, T.F.: A multiphase level set framework for image segmentation using the Mumford and Shah model. Int. J. Comput. Vision 50(3), 271–293 (2002)
Huiyan, J., Tan, H., Yang, B.: A priori knowledge and probability density based segmentation method for medical CT image sequences. BioMed Res. Int. (2014)
USGS, USGS Earth Explorer. http://earthexplorer.usgs.gov/ (2014). Accessed 3 Oct 2014
UMass Computer Vision Research Group. http://vis-www.cs.umass.edu/~vislib/Aerial/directory.html (2014). Accessed 23 Sept 2014
USCViterbi, University of Southern California. http://sipi.usc.edu/database/ (2014). Accessed 15 Sept 2014
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Fox, V., Milanova, M. (2016). An Evolutionary Optimization Control System for Remote Sensing Image Processing. In: Kountchev, R., Nakamatsu, K. (eds) New Approaches in Intelligent Image Analysis. Intelligent Systems Reference Library, vol 108. Springer, Cham. https://doi.org/10.1007/978-3-319-32192-9_5
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DOI: https://doi.org/10.1007/978-3-319-32192-9_5
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