摘要

为了实现在遥感图像处理过程中准确地提取到有效地物信息, 缩短分类用时, 将卷积神经网络（CNN）模型引入遥感图像地物分类, 首先提出由图片模糊加权平均（PFWG）改进的CNN分类方法, 利用模糊几何聚类算法作为预处理单元对实验样本进行特征规划, 并对遥感地物信息进行多源特征决策, 简化了分类过程, 加快了CNN模型的收敛速度。实验结果表明, 利用PFWG改进的CNN分类方法总体分类精度达到了93.73%; Kappa系数为0.94。该方法有效地弥补了CNN自身对遥感图像分类不够细腻、表达效果差的缺点, 较好地完成了多光谱遥感图像分类任务, 同时具备一定抗干扰能力。

Abstract

In order to accurately achieve the effective ground information in the process of remote sensing image processing and shorten the classification time, the convolutional neural networks (CNN) model is introduced into the classification of remote sensing image features. First, the picture fuzzy weighted average (PFWG) improved CNN classification method is proposed. The fuzzy geometric clustering algorithm is used as a pre-processing unit to characterize the experimental samples, and for multi-source feature decision-making for remote sensing ground information. The classification process is simplified and the convergence of the CNN model is speeded up. The experimental results show that using PFWG improved CNN classification method, the overall classification accuracy reaches 93.73%, and the Kappa coefficient is 0.94. This method effectively compensates for the shortcoming of CNN itself which is not good enough for classification and has poor expression performance of remote sensing images. It has successfully completed an efficient classification task and has a certain anti-jamming capability.

补充资料

中图分类号：TP751.1

DOI：10.3788/LOP56.031003

所属栏目：图像处理

基金项目：国家自然科学基金(61373112)、住房和城乡建设部科学技术项目计划（2016-R2-045）、陕西省自然科学基础研究资金（2014JM8348）

收稿日期：2018-06-14

修改稿日期：2018-07-14

网络出版日期：2018-08-15

作者单位    点击查看

联系人作者：樊潭飞(fantanfei1994@163.com)

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引用该论文

Wang Min,Fan Tanfei,Yun Weiguo,Wang Zhihui. PFWG Improved CNN Multispectra Remote Sensing Image Classification[J]. Laser & Optoelectronics Progress, 2019, 56(3): 031003

王民,樊潭飞,贠卫国,王稚慧. PFWG改进的CNN多光谱遥感图像分类[J]. 激光与光电子学进展, 2019, 56(3): 031003

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