Design and realization of wetland remote sensing image segmentation method

doi:10.3778/j.issn.1002-8331.2009.25.049

Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (25): 160-162.DOI: 10.3778/j.issn.1002-8331.2009.25.049

• 图形、图像、模式识别 • Previous Articles Next Articles

Design and realization of wetland remote sensing image segmentation method

SHI Yue-zhen¹，XIN Dong-jun²

1.School of Water Conservancy，Changsha University of Technology & Science，Changsha 410076，China
2.Computer Science College，Central South University of Forestry Technology，Changsha 410004，China

Received:2008-11-20 Revised:2009-01-07 Online:2009-09-01 Published:2009-09-01
Contact: SHI Yue-zhen

湿地遥感图像分割算法设计及实现

石月珍¹，辛动军²

1.长沙理工大学水利工程学院，长沙 410076
2.中南林业科技大学计算机科学学院，长沙 410004

通讯作者: 石月珍

Abstract

Abstract: A new effective multi-thresholds image segmentation method based on two-dimensional histogram FCM & entropy clustering is presented.Fuzzy C-means clustering algorithm has been widely used in automated image segmentation.However，the conventional FCM algorithm is noise sensitive because of not taking account of the spatial information.Fuzzy C-means clustering algorithm based on two-dimensional histogram is robust for noise，because it utilizes the gray level information of each pixel and its spatial correlation information within the neighborhood.The entropy term is introduced in object function that can suppress noise effectively and reduce influence of estimation of the cluster centers.Experimental results indicate that the new algorithm is better than FCM algorithm.

Key words: fuzzy C-means clustering, two-dimensional histogram, entropy, remote sensing image, image segmentation

摘要： 提出了一种结合熵和模糊C均值的聚类分割方法。模糊C均值（FCM）聚类算法广泛用于图像的自动分割，但是传统的FCM算法没有考虑像素的空间信息，因而对噪声十分敏感，基于二维直方图的模糊C均值聚类算法除了考虑像素点的灰度信息外还考虑了像素点邻域的空间信息，可有效地抑制噪声；在目标函数中引入熵项则能更好地抑制噪声和外围点对类中心估计的影响。实验分析结果表明，算法对湿地遥感图像的分割效果优于FCM算法。

关键词: 模糊C均值聚类, 二维直方图, 熵, 遥感图像, 图像分割

CLC Number:

TP391

SHI Yue-zhen¹，XIN Dong-jun². Design and realization of wetland remote sensing image segmentation method[J]. Computer Engineering and Applications, 2009, 45(25): 160-162.

石月珍¹，辛动军². 湿地遥感图像分割算法设计及实现[J]. 计算机工程与应用, 2009, 45(25): 160-162.

[1]	ZHANG Zhentong, SHAN Yugang, YUAN Jie. Remote Sensing Image Detection Algorithm Combining Multi-scale and Attention Mechanism [J]. Computer Engineering and Applications, 2021, 57(9): 212-216.
[2]	ZHAO Yang, ZHANG Junhua. Multi-scale Feature Fusion Method for Spinal X-Ray Image Segmentation [J]. Computer Engineering and Applications, 2021, 57(8): 214-219.
[3]	PAN Peixin, PAN Zhongliang. Active Contour Image Segmentation Combined with Saliency [J]. Computer Engineering and Applications, 2021, 57(8): 225-230.
[4]	DONG Peng, ZHOU Feng, ZHAO Congcong, WANG Yafei, MI Zetian, FU Xianping. Automatic Measurement of Underwater Sea Cucumber Size Based on Binocular Vision [J]. Computer Engineering and Applications, 2021, 57(8): 271-278.
[5]	SHI Chuntian, ZENG Yanyang, HOU Shouming. Summary of Application of Swarm Intelligence Algorithms in Image Segmentation [J]. Computer Engineering and Applications, 2021, 57(8): 36-47.
[6]	WEI Wei, YANG Ru, ZHU Ye. Target Detection of Improved CenterNet to Remote Sensing Images [J]. Computer Engineering and Applications, 2021, 57(6): 191-199.
[7]	NI Zongjun, CHEN Hui, ZHANG Yun, SU Min, ZHENG Xiujuan. Non-contact Physiological Parameter Estimation Method Based on Adaptive Denoising [J]. Computer Engineering and Applications, 2021, 57(5): 153-160.
[8]	CAI Dongli, ZHONG Qinghua, ZHU Yongsheng, LIAO Jinxiang, HAN Maizhi. EEG Emotion Recognition Using Convolutional Neural Network with 3D Input [J]. Computer Engineering and Applications, 2021, 57(5): 161-167.
[9]	WANG Peng, YE Xueyi, WANG Tao, QIAN Dingwei. Face Recognition Based on Double Variation and Double Space Local Directional Pattern [J]. Computer Engineering and Applications, 2021, 57(4): 91-99.
[10]	SHE Hailong, XIE Shanjuan, ZOU Jingjie. 3D-CNN with Standard Score Dimensionality Reduction for Hyperspectral Remote Sensing Images Classification [J]. Computer Engineering and Applications, 2021, 57(4): 169-175.
[11]	PENG Jing, LUO Haoyu, ZHAO Gansen, LIN Chengchuang, YI Xusheng, CHEN Shaojie. Survey of Medical Image Segmentation Algorithm in Deep Learning [J]. Computer Engineering and Applications, 2021, 57(3): 44-57.
[12]	MA Manfu, GUO Chenbiao, LI Yong, ZHANG Zhongying, ZHANG Qiang, WANG Changqing. Research on Heterogeneity of Attention Flow Network Based on Structural Entropy [J]. Computer Engineering and Applications, 2021, 57(23): 98-105.
[13]	LI Daidong, HE Xiaohui, LI Panle, TIAN Zhihui, ZHOU Guangsheng. Road Extraction Network of Remote Sensing Image Based on SPUD-ResNet [J]. Computer Engineering and Applications, 2021, 57(23): 240-247.
[14]	YI Lingzhi, WANG Shitong, YI Fang, DENG Dong, YI Zhimin, JIANG Peng. Wind Farm Ultra-Short-Term Wind Speed Prediction Based on EEMDSE-ILSTM [J]. Computer Engineering and Applications, 2021, 57(22): 288-294.
[15]	LIU Rui, DING Hui, SHANG Yuanyuan, SHAO Zhuhong, LIU Tie. COVID-19 Medical Imaging Dataset and Research Progress [J]. Computer Engineering and Applications, 2021, 57(22): 15-27.

Design and realization of wetland remote sensing image segmentation method

湿地遥感图像分割算法设计及实现

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics