激光与光电子学进展, 2018, 55 (2): 021001, 网络出版: 2018-09-10  

一种结合深度置信网络与最优尺度的植被提取方法 下载: 978次

Method of Vegetation Extraction Based on Deep Belief Network and Optimal Scale
作者单位
1 河南大学环境与规划学院, 河南 开封 475004
2 南昌工程学院江西省水信息协同感知与智能处理重点实验室, 江西 南昌 330099
摘要
针对利用现有深度学习方法进行植被提取时出现的相邻地物处于同一窗口、分类结果出现一些无用破碎图斑和“椒盐现象”等问题,提出最优分割尺度与深度置信网络相结合的方法进行植被提取研究,并利用光谱-纹理特征等信息进行对比实验。实验结果表明,与现有的深度学习方法相比,本文方法分类结果的总体精度达到91.92%,Kappa系数为0.8677,能够有效提高实验的分类精度,而且分类结果显示本文方法能有效减轻“椒盐现象”,并能很好地表达影像上各类地物清晰的边界。
Abstract
When using the existing methods of depth learning to study the vegetation extraction, there are some problems that the adjacent objects are in the same window, and some useless crushing plots and the salt and pepper phenomenon appear. We propose a method by combining the optimal segmentation scale with the deep belief network to study the vegetation extraction, and comparison experiments are carried out with spectral-texture features and other information. Experimental results show that the overall accuracy of the proposed method is 91.92% and the Kappa coefficient is 0.8677, and the proposed method can effectively improve the classification accuracy compared with the existing deep learning methods. The classification results show that the proposed method can effectively reduce the salt and pepper phenomenon, and clear express the boundaries of objects.

刘祖瑾, 杨玲, 刘祖涵, 段琳琳, 乔贤贤, 龚娇娇. 一种结合深度置信网络与最优尺度的植被提取方法[J]. 激光与光电子学进展, 2018, 55(2): 021001. Zujin Liu, Ling Yang, Zuhan Liu, Linlin Duan, Xianxian Qiao, Jiaojiao Gong. Method of Vegetation Extraction Based on Deep Belief Network and Optimal Scale[J]. Laser & Optoelectronics Progress, 2018, 55(2): 021001.

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