山东农业大学学报（自然科学版）Issue(4)：600-606,7.DOI:10.3969/j.issn.1000-2324.2015.04.025
基于乡镇尺度Landsat8 OLI影像融合算法适应性研究
Study on the Suitability of Landsat8 OLI Image for Fusion Algorithms Based on the Township Scale
摘要
Abstract
In this study, we used the OIF factor to choose the best MS band combination for Landsat8 OLI image at the township scale, aiming to study the suitability of 6 kinds of fusion algorithms including standard color variation method (Brovey method), the principal component transformation method (PCA method), Daubechies transformation method for wavelet, Coifet wavelet transformation method, transformation method combining HIS with wavelet and PCA combining with wavelet transformation method for merging of MS and PAN brands of OLI images, and classified the image before and after merging to verify the validity of the fusion results in actual production application with SVM method. Results showed that the B456 brand which OIF value was 27.842 was the best band combination among 35 kinds of combinations of 7 bands. Qualitative and quantitative accuracy assessment before and after merging image showed that each index of OLI image was the dominated for PCA algorithm, which had the highest merging adaptation. And the spectral distortion degree of Daubechies wavelet algorithm was the smallest; HIS-wavelet algorithm had the highest sharpness; PCA-wavelet algorithm had the highest correlation coefficient and merging information contents compared with others. Brovey was the worst adaptive algorithm among 6 kinds of fusion algorithms;Accuracy verification of land use classification demonstrated that OLI image which merged by PCA algorithm would contribute to improve the classification accuracy.关键词
OLI影像/融合算法/适应性/应用研究/乡镇尺度Key words
OLI images/merging algorithms/suitability/application research/township scale分类
信息技术与安全科学引用本文复制引用
黄安,王旭红,杨联安,杜挺,王元元,刘建红..基于乡镇尺度Landsat8 OLI影像融合算法适应性研究[J].山东农业大学学报（自然科学版）,2015,(4):600-606,7.基金项目
西北大学“211工程”研究生自主创新项目(YZZ13002) （YZZ13002）
国家自然科学基金(41071271) （41071271）
