西南林业大学学报2017，Vol.37Issue(4)：156-166,11.DOI:10.11929/j.issn.2095-1914.2017.04.023
基于WorldView-2影像数据对杭州西湖区绿地信息提取研究
Extraction of the Urban Green Space Based on WorldView-2 Images in West Lake District of Hangzhou
摘要
Abstract
According to the difference of objects in the WorldView-2 imagery in West Lake District of Hang-zhou, sub-regions were divided. Within each partition, different multi-scale segmentation was used and a hierarchi-cal structure was built. To make a comprehensive utilization of spectrum, shape and texture features of variables, the CART ( classification and regression trees) decision tree classification algorithm was constructed to select the optimal characteristics and thresholds for each sub-region to map the entire green space of West Lake District. To determine the texture window size and optimize the texture features, the method of J-M ( Jeffries-Matusita) distance was used. The results showed that with the method of J-M distance, the texture window size of grassland, agricultur-al land, shrubs and trees was 5 × 5, 11 × 11, 13 × 13, 13 × 13, respectively. It greatly improved the precision and efficiency of information extraction for the selection of texture window size and dimension of texture features. Compa-ring with the maximum likelihood method classification based on pixel, the overall accuracy was increased from 76. 53% to 88. 56%, and the kappa coefficient was improved from 0. 7117 to 0. 8623, the average user accuracy of green space was also increased from 72. 73% to 84. 63%;Comparing with the conventional object-oriented meth-od, the proposed method is more quickly flexible to determine features and thresholds, greatly improving the effi-ciency and accuracy of classification.关键词
区域/城市绿地/信息/J-M距离/决策树/特征变量Key words
region/urban green space/information/J-M distance/decision tree/characteristic variable分类
农业科技引用本文复制引用
钱军朝,徐丽华,邱布布,陆张维,庞恩奇,郑建华..基于WorldView-2影像数据对杭州西湖区绿地信息提取研究[J].西南林业大学学报,2017,37(4):156-166,11.基金项目
浙江省自然科学基金项目 ( LY15D010006) 资助 （ LY15D010006）
国家自然科学基金项目 ( E080201) 资助 （ E080201）
浙江省林学一级重中之重学科学生创新计划项目 (201516) 资助. （201516）
