文献类型：期刊文献

英文题名：Spectral-Spatial Hyperspectral Image Classification Based on Mathematical Morphology Post-Processing

作者：Hu, Lishuan[1,2]; Qi, Chengming[2]; Wang, Qun[1]

第一作者：Hu, Lishuan;胡立栓

通讯作者：Qi, Chengming

机构：[1] School of Information Engineering, China University of Geosciences [Beijing], Beijing, 100083, China; [2] College of Urban Rail Transit and Logistics, Beijing Union University, Beijing, 100101, China

第一机构：School of Information Engineering, China University of Geosciences [Beijing], Beijing, 100083, China

年份：2018

卷号：129

起止页码：93-97

外文期刊名：Procedia Computer Science

收录：EI(收录号：20182105220472)

语种：英文

外文关键词：Support vector machines - Morphology - Salt and pepper noise - Noise abatement - Classification (of information) - Spectroscopy - Image classification - Remote sensing

摘要：Hyperspectral remote sensing sensors can provide plenty of valuable information. Fusion of spectral and spatial information plays a key role in the field of HyperSpectral Image (HSI) classification. In this paper, a novel two stages spectral-spatial HSI classification method based on Mathematical Morphology (MM) post-processing is proposed. In first stage, Support Vector Machine (SVM) is adopted to obtain the initial classification results. In second stage, in order to remove salt and pepper noise, MM is used to refine the obtained results of above stage. Experiments are conducted on the Indian Pines dataset. The evaluation results show that the proposed approach achieves better accuracy than several recently proposed post-processing HSI classification methods. ? 2018 Elsevier Ltd. All rights reserved.