Acta Optica Sinica, Volume. 35, Issue s1, 110004(2015)
Application of Gaussian-Rayleigh Mixture Model in Remote Sensing Image Segmentation
[1] [1] Hou Y, Yang y, Rao N, et al.. Mixture model and Markov Random field-based remote sensing image unsupervised clustering method[J]. Opto-Electronics Review, 2011, 19(1): 83-88.
[2] [2] Fan Liheng, Lü Junwei, Deng Jiangsheng. Classification of hyperspectral remote sensing image based on bands grouping and classification ensembles[J]. Acta Optica Sinica, 2014, 34(9): 0910002.
[3] [3] Su W, Li J, Chen Y, et al.. Textural and local spatial statistics for the object goriented classification of urban areas using high resolution imagery[J]. International Journal of Remote Sensing, 2008, 29(11): 3105-3117.
[5] [5] Zhong P, Wang R. A multiple conditional Random fields ensemble model for urban area detection in remote sensing optical images[J]. Geoscience and Remote Sensing, IEEE Transactions on, 2007, 45(12): 3978-3988.
[6] [6] Fan J, Han M, Wang J. Single point literative weighted fuzzy C-means clustering algorithm for remote sensing image segmentation[J]. Pattern Recognition, 2009, 42(11): 2527-2540.
[7] [7] Mylonas S K, Stavrakoudis D G, Theocharis J B. GeneSIS: A GA-based fuzzy segmentation algorithm for remote sensing images[J]. Knowledge-Based Systems, 2013, 54(12): 86-102.
[8] [8] Wan F, Deng F. Remote sensing image segmentation using mean shift method[J]. Simulation and Modeling Communications in Computer and Information Science, 2011, 176(11): 86-90.
[9] [9] Pal M, Maxwell A E, Warner T A. Kernel-based extreme learning machine for remote-sensing image classification[J]. Remote Sensing Letters, 2013, 4(9): 853-862.
[10] [10] Du Peijun, Chen Yu, Xia Junshi, et al.. A novel remote sensing image classification scheme based on data fusion, multiple features and ensemble learning[J]. Journal of Indian Soc Remote Sens, 2013, 41(2): 213-222.
[11] [11] Nikou C, Galatsanos N P, Likas A C. A class-adaptive spatially variant mixture model for image segmentation[J]. Image Processing, IEEE Transactions on, 2007, 16(4): 1121-1130.
[12] [12] Lunga D, Ersoy O. Kent mixture model for classification of remote sensing data on spherical manifolds[C]. Applied Imagery Pattern Recognition Workshop, IEEE, 2011: 1-7.
[13] [13] Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov Random field model and the expectation-maximization algorithm[J]. Medical Imaging, IEEE Transactions on, 2001, 20(1): 45-57.
[14] [14] Deng H, Clausi D A. Unsupervised image segmentation using a simple MRF model with a new implementation scheme[J]. Pattern Recognition, 2004, 37(12): 2323-2335.
[15] [15] Zhou S, Chen W, Jia F, et al.. Segmentation of brain magnetic resonance angiography images based on MAP-MRF with multi-pattern neighborhood system and approximation of regularization coefficient[J]. Medical Image Analysis, 2013, 17(8): 1220-1235.
[16] [16] Seng C H, Bouzerdoum A, Amin M G, et al.. A Gaussian-Rayleigh mixture modeling approach for through-the-wall radar image segmentation[C]. Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on, 2012: 877-880.
[17] [17] Debes C, Amin M, Zoubir A. Target detection in singleand multiple-view through-the-wall radar imaging[J]. Geoscience and Remote Sensing, IEEE Transactions on, 2009, 47(5): 1349-1361.
[18] [18] Tao W B, Tian J W, Liu J. Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm[J]. Pattern Recognition Letters, 2003, 24(16): 3069-3078.
[19] [19] Vivone G, Addesso P, Conte R, et al.. A class of cloud detection algorithms based on a MAP-MRF approach in space and time[J]. Geoscience and Remote Sensing, IEEE Transactions on, 2014, 52(8): 5100-5114.
[20] [20] Borges J, Bioucas-Dias J, Marcal A. Bayesian hyperspectral image segmentation with discriminative class learning[J]. Geoscience Remote Sensing, IEEE Transactions on, 2011, 49(6): 2151-2164.
[21] [21] Zhang B, Li S, Jia X, et al.. Adaptive Markov random field approach for classification of hyperspectral imagery[J]. Geoscience Remote Sensing Letters, IEEE, 2011, 8(5): 973-977.
[22] [22] Hegarat-Mascle S Le, Andre C. Use of Markov random fields for automatic cloud/shadow detection on high resolution optical images[J]. ISPRS J Photogramm Remote Sens, 2009, 64(4): 351-366.
Get Citation
Copy Citation Text
Hou Yimin, Tang Yue, Sun Xiaoxue, Sui Wenxiu. Application of Gaussian-Rayleigh Mixture Model in Remote Sensing Image Segmentation[J]. Acta Optica Sinica, 2015, 35(s1): 110004
Category: Image Processing
Received: Jan. 25, 2015
Accepted: --
Published Online: Jul. 27, 2015
The Author Email: Yimin Hou (ymh7821@163.com)