浙江农林大学学报2017，Vol.34Issue(2)：340-348,9.DOI:10.11833/j.issn.2095-0756.2017.02.019
面向林地分类的GF-2影像融合算法评价
Comparison of fusion algorithms for GF-2 data from extracted forestland information
摘要
Abstract
To obtain an optimal method for image enhancemen t of GF-2 forestry area data, six frequently-used methods were analyzed: Brovey transformation; hue, saturation, and value (HSV) transformation; Principle Component (PC) spectral sharpening; high pass filter (HPF) spectral sharpening; Gram-Schmidt spectral sharpening; and Pansharp transformation. Qualitative and quantitative analyses were used to assess the effect and quality of the fusion images. Indexes include mean, average gradient, high-frequency information integra-tion, correlation index, entropy index and second moment index. Among them, correlation index and second moment index were calculate by ENVI, other indexes were all by Matlab. Furthermore, to access an appropriate fusion method for GF-2 forestland data extraction, fusion images were classified by performance of fusion meth-ods at two information extraction levels based on an object-oriented classification method. All the transforma-tions used the same parameter and methods on each level, and use the same samples to classify and accuracy check. Results showed that correlation index and high-frequency information integration of HSV transformation could reach 0.823 and 0.570, respectively. In addition, the entropy index and second moment index could im-proved 25% and 50% compared to original multiple image, respectively. It had a better visual effect with obvi-ous enhanced clarity and texture features. For classification experiments, HSV and Brovey transformations had their own superiority for the extraction of different classes with the HSV transformation having the highest over-all classification accuracy of 85.1% and the Brovey transformation having the highest accuracy on the second level of 75.7%. The other four methods had different advantages for quality and information extraction of the fusion images. Thus, the final selection of fusion methods should consider practical forestry application and im-age information which could provide a reference for GF-2 images to be applied on a large scale in forested ar-eas.关键词
森林经理学/影像融合/林地提取/GF-2影像/面向对象分类Key words
forest management/image fusion/forestland/GF-2 data/object-oriented image classification分类
农业科技引用本文复制引用
胡曼,彭道黎..面向林地分类的GF-2影像融合算法评价[J].浙江农林大学学报,2017,34(2):340-348,9.基金项目
国家重点林业工程监测技术示范推广项目(2015-02) （2015-02）
