摘要
针对高分辨率遥感影像地物分布复杂多变,利用ELM的快速分类性能,提出了一种ELM的多特征多核高分辨率遥感影像分类方法。首先利用多尺度分割算法将原始影像粗分为若干地物区域;然后依据区域合并准则对粗分割图像合并得到典型地物特征的对象信息,并提取分割对象的光谱特征与空间特征;最后以多种核函数加权组合的方式构建多核ELM对影像分类,获得最终的分类结果。实验结果表明,所提方法不仅降低了对目标训练样本的要求,同时还提高了分类的准确性、及时性和完整性。
Given the complex and variable distribution of high-resolution remote sensing images and the fast classification performance of the extreme learning machine(ELM),we propose a multi-feature multi-kernel high-resolution remote sensing image classification method based on ELM.Firstly,the original image is roughly divided into several feature regions by the multi-scale segmentation algorithm.Then the object information of typical earth features is obtained by merging the coarse segmentation images according to the region merging criterion,and the spectral features and spatial features of the segmentation objects are extracted.A multi-kernel ELM via weighted combination of kernel functions is used to classify images,and the final classification results are obtained.Experimental results show that the proposed method not only reduces the requirements for the target training samples,but also improves the accuracy,timeliness and integrity of the classification.
作者
楚恒
蔡衡
单德明
CHU Heng;CAI Heng;SHAN De-ming(School of Communication and Information Engineering,Chongqing University of Posts and Telecommunications,Chongqing 400065;Key Laboratory of Optical Communication and Network in Chongqing,Chongqing 400065;Key Laboratory of Ubiquitous Sensing and Networking in Chongqing,Chongqing 400065;Chongqing Survey Institute,Chongqing 400020,China)
出处
《计算机工程与科学》
CSCD
北大核心
2019年第10期1816-1822,共7页
Computer Engineering & Science
基金
重庆高校创新团队建设计划(CXTDX201601020)
关键词
高分辨率遥感影像
极限学习机
多尺度分割
区域合并
核函数
high resolution remote sensing image
extreme learning machine
multi-scale segmentation
region merging
kernel function
