文献类型：会议论文

英文题名：An automatic optimization method of forest type classification using PolSAR image based on genetic algorithm

作者：Xu, Kunpeng[1] Chen, Erxue[1] Li, Zengyuan[1] Zhao, Lei[1] Wan, Xiangxing[1] Wen, Zhe[1]

第一作者：Xu, Kunpeng

机构：[1] Institute of Forest Resources Information Technique, Chinese Academy of Forestry, Beijing, China

会议论文集：2019 6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

会议日期：November 26, 2019 - November 29, 2019

会议地点：Xiamen, China

语种：英文

外文关键词：Classification (of information) - Forestry - Genetic algorithms - Image enhancement - Polarization - Support vector machines - Synthetic aperture radar

年份：2019

摘要：In order to improve the performance of nonparametric classifier on high dimensional polarization features set, an automatic optimization method based on genetic algorithm is proposed, and is used for polarimetric synthetic aperture radar (PolSAR) image forest land type classification. The method focusing on two main aspects that affect the classification performance, which are features combination and model hyperparameter. Different from conventional process which optimize those two aspects respectively. Our proposed method using genetic algorithm as searching engine, by regarding features combination and hyperparameters as a set of model settings. We can optimize those two aspects simultaneously, so that the synergy affect between those two aspects can be considered. A PolSAR image (C-band, quad polarization) data were used to verify the proposed optimization method using support vector machine (SVM) as classifier. ? 2019 IEEE.