干旱区生态环境知识资源中心(Arid): 干旱区Landsat8全色与多光谱数据融合算法评价

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	干旱区Landsat8全色与多光谱数据融合算法评价
其他题名	Fusion algorithm evaluation of Landsat 8 panchromatic and multispetral images in arid regions
	杨丽萍 1; 马孟 2; 谢巍 2; 潘雪萍 2
来源期刊	国土资源遥感
ISSN	1001-070X
出版年	2019
卷号	31 期号:4 页码:11-19
中文摘要	针对目前Landsat8影像像素级融合算法中单因素评价指标对比性不强、置信度较低、难以实现融合效果综合评估的问题,基于居延泽地区的Landsat8影像,采用11种单因素指标和面向对象分类方法,从空间信息量、光谱特征及地物分类精度3个方面综合评价了主成分变换法(principle component transform,PC) 、比值变换法(brovey transform, BT) 、HSV(hue - saturation - value)变换法、相位恢复变换法(Gram - Schmidt pan sharpening,G - S) 、高通滤波算法(high pass filtering,HPF)和小波变换法(wavelet transform,WT)等6种融合算法的融合效果。结果表明,各融合算法的空间分辨率及纹理特征相较于原始影像均得到增强。HSV法表达空间细节的能力最佳,但其光谱保真度较差; WT法可最大程度地保持光谱特征,且空间细节表达能力仅次于HSV法,最适用于Landsat 8的影像融合;综合考虑空间信息量与光谱特征,PC法和G - S法效果适中,略低于HPF法,BT法最劣。从分类结果来看,WT法和HPF法的分类精度相较于原始影像的分类精度有一定的提高。
英文摘要	With lower contrast and confidence level,single factor evaluation index is not very effective in the comprehensive evaluation of pixel level image fusion algorithms of Landsat 8 in arid regions. Based on the Landsat 8 image of Juyanze area,11 single factor indicators and object - oriented classification method were used to compare the following six image fusion algorithms,i. e., Principal Component (PC), Brovey Transform (BT), Hue - Saturation - Value Transform (HSV), Gram - Schmidt Pan Sharpening (G - S), High - pass filtering (HPF) and Wavelet Transform (WT) according to the spatial information quantity, spectral feature and classification accuracy. The results indicate that the spatial resolution and texture features of all fusion images are enhanced in comparison with the original image. HSV is proved to be the best algorithm to highlight the texture features in arid regions,but its spectral fidelity is bad. WT exhibits an excellent capability in maintaining the spectral information,and its capability of revealing spatial details is just next to the HSV method. Therefore,WT is considered the most suitable algorithm for image fusion of Landsat 8 in this study. Taking the spatial information quantity and spectral features into account simultaneously,the authors hold that PC and G - S have moderate performance,and their performance is a little lower than that of HPF,while the performance of BT is the worst. The classification results show that the classification accuracy of WT and HPF is improved to some extent compared with the original image.
中文关键词	图像融合 ; 光谱信息 ; 空间信息 ; 面向对象分类 ; 效果评价
英文关键词	fusion algorithm spectral information spatial information object - oriented classification effect evaluation
语种	中文
收录类别	CSCD
WOS类目	REMOTE SENSING
WOS研究方向	Remote Sensing
CSCD记录号	CSCD:6623445
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/315778
作者单位	1.长安大学地质工程与测绘学院, 西安, 陕西 710054, 中国; 2.长安大学地球科学与资源学院, 西安, 陕西 710054, 中国
推荐引用方式 GB/T 7714	杨丽萍,马孟,谢巍,等. 干旱区Landsat8全色与多光谱数据融合算法评价[J],2019,31(4):11-19.
APA	杨丽萍,马孟,谢巍,&潘雪萍.(2019).干旱区Landsat8全色与多光谱数据融合算法评价.国土资源遥感,31(4),11-19.
MLA	杨丽萍,et al."干旱区Landsat8全色与多光谱数据融合算法评价".国土资源遥感 31.4(2019):11-19.