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	基于卷积神经网络的不良地质体识别与分类
其他题名	Identification and Classification of Adverse Geological Body Based on Convolution Neural Networks
	陈冠宇; 安凯; 李向
来源期刊	地质科技情报
ISSN	1000-7849
出版年	2016
卷号	35 期号:1 页码:205-211
中文摘要	西部大开发战略实施以来,西部地区,尤其是新疆等地,修建高速公路成为近年来的首要任务。但是,西部海拔较高,地理环境恶劣,像沙害、盐渍土、冻土和荒漠等特殊地质体广泛分布。以新疆尉犁县罗布人村寨为研究区域,针对当地典型的不良地质体遥感影像特征,重点探讨了深度学习算法中的卷积神经网络方法在不良地质体识别与分类中的应用,实验结果对比分析表明:与K-均值分类器、SVM分类器和贝叶斯分类器对比分类精度,当样本数量较少时卷积神经网络方法优势不明显,当训练样本足够大时,其分类精度达到90%左右,优势非常明显。
英文摘要	Since China’s implementation of the western development strategy, in the western region, especially in Xinjiang,the construction of highways become a top priority in recent years. However, the western high altitude,harsh geographical environment, the special geological body such as comprehensive, saline soil,frozen soil and widely distributed desert always block the development. This article,taking Yuli Rob village of Xinjiang as the study area,studies the application of the convolution neural network method in deep learning algorithms for the identification and classification of adverse geological bodies based on the remote sensing image characteristics of local typical adverse geology. The experimental results analysis showed that:compared with K-mean classifier,SVM classifier and Bayesian classifier classification,the advantage of convolution neural network is not obvious with small number of samples, but its advantage is remarkable,when the training sample is large enough,with the classification accuracy reaching about 90%.
中文关键词	遥感影像 ; 不良地质体 ; 深度学习 ; 分类方法 ; 神经网络
英文关键词	remote sensing image adverse geological body deep learning classification method neural networks
语种	中文
国家	中国
收录类别	CSCD
WOS类目	METEOROLOGY ATMOSPHERIC SCIENCES
WOS研究方向	Meteorology & Atmospheric Sciences
CSCD记录号	CSCD:5620279
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/234114
作者单位	中国地质大学(武汉)计算机学院, 武汉, 湖北 430074, 中国
推荐引用方式 GB/T 7714	陈冠宇,安凯,李向. 基于卷积神经网络的不良地质体识别与分类[J],2016,35(1):205-211.
APA	陈冠宇,安凯,&李向.(2016).基于卷积神经网络的不良地质体识别与分类.地质科技情报,35(1),205-211.
MLA	陈冠宇,et al."基于卷积神经网络的不良地质体识别与分类".地质科技情报 35.1(2016):205-211.