摘要
在比较了学习矢量量化(LVQ)算法和广义学习矢量量化(GLVQ)算法的基础上,建立了基于GLVQ的遥感影像分类模型。以实际土地覆盖分类为例,通过与传统统计方法和LVQ分类器比较,GLVQ分类器具有分类正确率高,收敛速度快,适应范围广等优点。
After comparing Leaming Vector Quantization (LVQ) algorithm and Generalized Learning Vector Quantization (GLVQ) algorithm, this paper establishes a GLVQ-based classification model for remote sensing image. With the experimental applications of land-over classification with the presented model, the GLVQ classifier has higher recognition rate, faster convergence speed and wider adaption range than conventional classifier and LVQ classifier.
出处
《电子与信息学报》
EI
CSCD
北大核心
2006年第7期1201-1203,共3页
Journal of Electronics & Information Technology
基金
辽宁省自然科学基金(20022032)资助课题
关键词
遥感影像分类
学习矢量量化(LVQ)
广义学习矢量量化(GLVQ)
Remote sensing image classification, Learning Vector Quantization(LVQ), Generalized Learning Vector Quantization (GLVQ)