COMPARISON OF IHS TRANSFORMATION FOR INTEGRATING SAR AND TM IMAGES

doi:10.6046/gtzyyg.1997.03.06

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Abstract In this paper, four kinds of IHS transformations are used to integrate SAR and TMimages. The resultant images are evaluated qualitatively and quantitatively. It shows that the quantitative analysis using entropy, joint entropy and average gradient is better than visual qualitative analysis, and provides the way to choose the most suitable IHStransformation for conbining images.

Keywords Time-series images NDVI Fourier analysis

Issue Date: 02 August 2011

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	WANG Dan
	JIANG Xiao-Guang
	TANG Ling-Li
	XI Xiao-Huan
	JIA Hui
	HE Zheng-Qin
	CHEN Yi-Jun
	ZHANG Hui
	LIU Guo
	SUN Zeng-Wei

Cite this article:

WANG Dan,JIANG Xiao-Guang,TANG Ling-Li, et al. COMPARISON OF IHS TRANSFORMATION FOR INTEGRATING SAR AND TM IMAGES[J]. REMOTE SENSING FOR LAND & RESOURCES, 1997, 9(3): 34-39.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.1997.03.06 OR https://www.gtzyyg.com/EN/Y1997/V9/I3/34

[1] 国家科委国家遥感中心编,空间遥感技术综合应用预测及效益分析,学术期刊出版社,1988

[2] 李旭文主成分变换和彩色变换在TM图像信息提取中的应用——以苏州市为例。《环境遥感》,17(4),1992

[3] Conrac Corp.,Conrac Division. Raster Graphics Handbook. Covina, California. Conrac Corp,1980

[4] Harris, J. R. Murray, and T. Hirose. IHS Transformation for the Integration of Radar Imagery with Other Remotely Sensed Data. PE&RS, Vol 56, No.12, 1990

[5] 宋顺林编著.计算机动画技术南京:南京大学出版社,1995

[6] Kay S. P. HSI Color Image Processing Techniques and Applications,mage Processing, . Handbook, Data Translation, Inc,1990

[7] Haydn, R.,and Other. Application of the IHS Color Transformation to the Processing of Multisensor Data and Image Enhancement. PMC, ISRSAS&SL, Cairo Rgypt.1982

[9] 郭华东主编.雷达图像分析与地质应用北京:科学出版社,1991

[9] Chavez, Pat S. Stuart C. Sides and Jeffrey A. Anderson. Comparison of three Different Methods to Merge Multi-resolution

and Multispectral Data Landsat TM and Spot Panchromatic. PE&RS, Vol 57, No. 3, 1991

[10] Curtis, Munedhika, Jameo S. Warnick, Carl Salvaggio etc. Resoulution Enhancement of Multispectral Image Date to Imtpove Classification AccmCY. pe&rs, vOL lix, nO.1. 1993

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