中南民族大学学报（自然科学版）2016，Vol.35Issue(1)：95-102,8.
基于BKNNSVM算法的高分辨率遥感图像分类研究
The Classification of the High Resolution Remote Sensing Images with BKNNSVM
摘要
Abstract
In order to solve the deficiency about the algorithm of the local support vector machine ( KNNSVM ) which is very time-consuming on classifying the high resolution remote sensing images with mass data and improve the performance of the KNNSVM, the BKNNSVM algorithm based on uncertainty is proposed. The algorithm applies the binomial distribution of conjugate prior Beta distribution to estimate probability belong to the class or negative of each unlabeled sample through its nearest neighbor distribution. Then, with threshold, some unlabeled samples are classified by the KNN when their uncertainty value is less than threshold and others are classified by the local SVM when their uncertainty value is more than threshold. The experiments on the actual high resolution remote sensing images have shown that BNNSVM can decrease the time consuming effectively and keep the precision of the original KNNSVM with suitable threshold of uncertainty.关键词
高分辨率遥感图像分类/KNNSVM算法/BKNNSVM算法Key words
high resolution remote sensing images/KNNSVM/BKNNSVM分类
信息技术与安全科学引用本文复制引用
舒振宇,周城,王典洪..基于BKNNSVM算法的高分辨率遥感图像分类研究[J].中南民族大学学报（自然科学版）,2016,35(1):95-102,8.基金项目
湖北省自然科学基金资助项目(PBZY14019) （PBZY14019）
