Unsupervised segmentation of high-resolution remote sensing images based on classical models of the visual receptive field

被引:3
作者
Xu, Miaozhong [1 ]
Cong, Ming [1 ]
Xie, Tianpeng [1 ]
Tao, Yiting [1 ]
Zhu, Xiaoling [1 ]
Zhao, Jingjing [1 ]
机构
[1] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430072, Peoples R China
关键词
log-Gabor (LG) filter; Markov random field (MRF) model; image segmentation; FACE RECOGNITION; ALGORITHMS; NOISY; EM;
D O I
10.1080/10106049.2015.1006529
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Here, we describe an unsupervised segmentation method incorporating log-Gabor (LG) filters and a Markov random field (MRF) model for high-resolution (HR) remote sensing (RS) images, based on classical models of the visual receptive field. LG filters were utilised to model the receptive fields of the simple cells in the primary visual cortex and extract detailed features from HR-RS images followed by construction of image pyramid through wavelet decomposition to simulate the hierarchical structure of the visual sensing system. Finally, based on the original HR-RS images, their detailed features and the image pyramid, the MRF image segmentation model was applied to obtain the final segmentation result. Real HR-RS images were used as experimental data to validate the proposed method, both qualitatively (visually) and numerically (with the overall accuracy and Kappa index).The experimental results indicate that the proposed method is effective, feasible and robust to noise.
引用
收藏
页码:997 / 1015
页数:19
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