融入空间关系的GMM全色高分辨率遥感影像监督分割方法

王春艳; 徐爱功; 孙川; 赵雪梅

doi:10.11999/JEIT160798

融入空间关系的GMM全色高分辨率遥感影像监督分割方法

doi: 10.11999/JEIT160798

1.
(辽宁工程技术大学矿业技术学院葫芦岛 125105) ②(辽宁工程技术大学测绘与地理科学学院阜新 123000) ③(阜新市国土资源局阜新 123000)

基金项目:

辽宁省教育厅一般项目(LJYL036, LJYL012)，教育部高等学校博士学科点专项科研基金(20122121110007)

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出版历程
- 收稿日期: 2016-07-26
- 修回日期: 2017-01-10
- 刊出日期: 2017-05-19

Surpervised Segmentation Algorithm Based on GMM with Spatial Relationship for High Resolution Ranchromatic Remote Sensing Image

1.
(School of Mining Industry and Technology, Liaoning Technical University, Huludao 125105 China)
2.
(School of Geomatics, Liaoning Technical University, Fuxin 123000, China)

Funds:

The General Science Research Project of Education Bureau of Liaoning Province (LJYL036, LJYL012), The Research Fund for the Doctoral Program of Higher Education of China (20122121110007)

摘要

摘要: 为了解决高分辨率遥感影像中相同地物目标异质性和空间破碎性增大及不同地物目标的相似性增强所带来的分割新问题，该文提出一种融入空间关系的高斯混合模型(GMM)高分辨遥感影像监督分割方法。该方法首先按分割区域进行监督采样，并通过最小二乘法进行直方图拟合，对影像中的每个类别区域建立GMM用来精确表征高分辨遥感影像每个分割区域复杂的地物光谱特征；然后在GMM的概率测度域融入空间关系，使每个像素的区域所属由该像素邻域窗口内所有像素概率测度共同决定，以刻画高分辨率遥感影像中像素间的空间相关性；最后按照最大概率测度原则完成对高分辨率遥感影像的分割。为了验证文中算法的可行性与有效性分别对合成影像及真实高分辨率遥感影像进行分割实验，并和经典的FCM方法及HMRF-FCM方法进行对比，定量与定性的结果证明了文中方法能够提高分割精度。
- 高分辨率遥感影像 /
- 高斯混合模型 /
- 空间关系 /
- 监督分割
Abstract: This paper proposes a supervised image segmentation algorithm for high resolution remote sensing images by introducing the Gaussian Mixture Model (GMM) with spatial relationship in order to solve the problem of the increasing dissimilarity in the same object and the decreasing of dissimilarity between two different objects. The proposed algorithm takes samples according to the segmentation areas and uses the least squared method to fit the histogram. GMMs are established to describe the complex spectral characteristic in each area accurately. Then spatial relationships are taken consider into the probability measures in GMMs to make the dissimilarities of pixels in a window is determined by all the pixels in the same window. Overall the GMMs can describe the spatial relationships between the pixels in high resolution remote sensing images. Finally the segmentation result is obtained by maximum probability principle. To verify the feasibility and the effectively of the proposed algorithm, the algorithm is performed on real high resolution remote sensing and synthetic images and compared the results with that of FCM and HMRF-FCM based segmentation algorithm. Qualitative and quantitative results prove that the proposed algorithm could improve the accuracy of segmentation.
- High resolution remote sensing image /
- Gaussian Mixture Model (GMM) /
- Spatial relationship /
- Supervised segmentation

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参考文献(21)

BRUZZONE L and CARLIN L. A multilevel context-based system for classification of very high spatial resolution images [J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(9): 2587-2600. doi: 10.1109/TGRS.2006.875360.

ACAR E and SELIM A. Anomaly detection with sparse unmixing and Gaussianmixture modeling of hyperspectral images[C]. IEEE Conference Publications, Italy, Milan, 2015: 5035-5038. doi: 10.1109/IGARSS.2015.7326964.

BRYAN P and DANIEL W. Bliss estimation information bounds using the I-MMSE formula and Gaussian mixture models[C]. Annual Conference on Information Science and Systems, Princeton, NJ, 2016: 274-279. doi: 10.1109/CISS. 2016.7460514.

WANG Xiaoyan, WANG Yangsheng, and FENG Xuetao, et al. Adaptive gaussian mixture models mased facial actions tracking[C]. International Conference on Computer Science and Software Engineering, Wuhan, China, 2008, 2: 923-926. doi: 10.1109/CSSE.2008.648.

WILSON S, KURUOGLU E, and SALERNO E. Fully bayesian source separation of astrophysical images modelled by mixture of gaussians[J]. IEEE Journal of Selected Topics in Signal Processing, 2008, 2(5): 685-696. doi: 10.1109/ JSTSP.2008.2005320.

AVI M and YUVAL B S. Gaussian mixture models for speaker-recognition[J]. IET Journals Magazines, 2014, 8(8): 860-867. doi: 10.1049/iet-spr.2013.0270.

ZIVKOVIC Z. Improved adaptive gaussian mixture model for background subtraction[C]. Proceedings of International Conference on Pattern Recognition, 2004, 2: 28-31. doi: 10. 1109/ICPR.2004. 1333992.

TAO Wenbing and SUN Kun. Robust point sets mtching by fusing feature and spatial information using nonuniform gaussian mixture models[J]. EEE Transactions on Image Processing, 2015, 24(11): 3754-3767. doi: 10.1109/TIP.2015. 2449559.

SFIKAS G, NIKOU C, and HEINRICH C, et al. Spatially varying mixtures incorporating line processes for image segmentation[J]. Journal of Marhematical Imaging and Vision, 2010, 36(2): 91-110. doi: 10.1007/s10851-009-0174-x.

MITRA S. Gaussian mixture models for human face recognition under illumination variations[J]. Applied Mathematics, 2012, 3: 2071-2079. doi: 10.4236/am.2012. 312A286.

Nadia Z, Hayet B, HAYET B, et al. Voice interaction using Gaussian mixture models for augmented reality applications[C]. 2015 4th International Conference on Electrical Engineering, Boumerdes, Algeria, 2015: 1-4. doi: 10.1109/INTEE.2015.7416773.

GROOSS R, YANG J, and WAIBEL A. Growing Gaussian mixture models for pose invariant face recognition[C]. Proceedings 15th International Conference on Pattern Recognition, Barcelona, Spain, 2000, 1: 1088-1091. doi: 10.1109/ICPR.2000.905661.

MONTAZERI GHAHJAVERESTAN M, MASOUDI S, SHAMSOLLAHI M B, et al. Coupled hidden markov model-based method for apnea bradycardia detection[J]. IEEE Journal of Biomedical and Health Informatics, 2016, 20(2): 527-538. doi: 10.1109/JBHI.2015.2405075.

GEMAN D and GEMAN D. Stochastic relaxation, gibbs distributions and the bayesian restoration of images[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1984, 6(6): 721-741. doi: 10.1109/TPAMI.1984.4767596.

赵雪梅, 李玉, 赵泉华. 结合高斯回归模型和隐马尔可夫随机场的模糊聚类图像分割[J]. 电子与信息学报, 2014, 36(11): 2730-2736. doi: 10.3724/SP.J.1146.2013.01751.

ZHAO Xuemei, LI Yu, and ZHAO Quanhua. Image segmentation by fuzzy clustering algorithm combining hidden markov random field and Gaussian regression model[J]. Journal of Electronics and Information Technology, 2014, 36(11): 2730-2736. doi: 10.3724/SP.J.1146.2013.01751.

CHOI H S, HARNOR D R, and KIM Y. Partial volume tissue classification of multichannel magnetic resonance imagesA mixel model[J]. IEEE Transactions on Medical Imaging, 1991, 10(3): 395-407. doi: 10.1109/42.97590.

AMIR H and JOSEP P. A doubly hierarchical dirichlet process hidden markov model with a non-ergodic structure[J]. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2016. 24(1): 174-184. doi: 10.1109/TASLP.2015. 2500732.

NGUYEN T M, WU Q M, and AHUJA S. An extension of the standard mixture model for image segmentation[J]. IEEE Transactions on Neural Networks, 2010, 21(8): 1326-1338. doi: 10.1109/TNN.2010.2054109.

熊涛, 姜万寿, 李乐林. 基于高斯混合模型的遥感影像半监督分类[J]. 武汉大学学报(信息科学版), 2011, 36(1): 108-112.

XIONG Tao, JIANG Wanlin, and LI Lelin. Gauss mixtre model based semi supervised classifiction for remote rensing image[J]. Geomatics and Information Science of Wuhan University, 2011, 36(1): 108-112.

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