复合类别支持的多元线性回归遥感影像色彩归一化方法

doi:10.3724/SP.J.1047.2016.00615

地球信息科学学报 ›› 2016, Vol. 18 ›› Issue (5): 615-621.doi: 10.3724/SP.J.1047.2016.00615

• 遥感大数据协同计算方法 • 上一篇下一篇

复合类别支持的多元线性回归遥感影像色彩归一化方法

吴炜¹(), 程熙², 顾国民¹

1. 浙江工业大学计算机学院,杭州 310023
2. 成都理工大学地球物理学院,成都 610059

收稿日期:2015-12-15 修回日期:2016-04-12 出版日期:2016-05-10 发布日期:2016-05-10
作者简介:
作者简介：吴炜(1985-),男,安徽太湖人,博士,讲师,研究方向为遥感信息处理与分析。E-mail:wuwei@zjut.edu.cn
基金资助:
国家自然科学基金项目(41301473);中国科学院重点部署项目(KZZD-EW-07-02)

Compound Cluster Center Based Multiple Linear Regression Color Normalization Method for Remote Sensing Image

WU Wei^1,^*(), CHENG Xi², GU Guomin¹

1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China
2. School of Geophysics, Chengdu University of Technology, Chengdu 610059, China

Received:2015-12-15 Revised:2016-04-12 Online:2016-05-10 Published:2016-05-10
Contact: WU Wei

摘要/Abstract

摘要：

受植被时相变化、传感器畸变、获取时刻大气条件等因素的影响,不同时间获取的遥感影像存在色彩差异,而逐波段的色彩归一化容易引起新的色彩畸变。因此,本文提出一种复合类别支持的多元线性回归遥感影像色彩归一化方法,在输入影像和参考影像逐波段高斯归一化的基础上,进行复合聚类,确定各像元的复合类别;在迭代去除变化像元的基础上,将类别中心作为控制点,建立多元线性回归方程,并据此对输入影像进行处理。2组影像的试验结果表明,本文方法相对于传统方法在整体精度、色彩保持等方面具有较大的优势。

关键词: 色彩归一化, 复合类别, 色彩保持, 多元线性回归

Abstract:

Due to the impacts of phonological change, sensor distortion, variation of atmospheric conditions and a lot of other factors, the images acquired at different time points for the same area are affected by color differences, Color normalization tries to eliminate /reduce color differences between different images and obtain seamless mosaic result. However, the traditional band-by-band normalization methods ignore the correlation between different bands and corrected each band independently, which may lead to new color distortion. To solve this problem, this paper presents a compound cluster center based multiple linear regression color normalization method for remote sensing image. Firstly, the source image and the reference images are primarily normalized based on the mean and variation values for every band and a new feature vector is constructed. Then, the compound clusters, which are extracted by unsupervised compound classification, are used to model the variation relationship between the two images. Afterwards,the outliers in every cluster may induce suddencolor change between the images of different time, so the outliers is identified and excluded. Last, the mapping relationship between the source image and the reference image is established with respect to the centers of clusters andall bands of source image are corrected simultaneously. The proposed method has been applied to two datasets with different land cover and spatial resolution, and results show that the proposed method can obtain color consistency result. Compared with the result of traditional method, our method over performsin preserve color and overall precision.

Key words: color normalization, compound cluster, color preserve, multiple linear regression

吴炜, 程熙, 顾国民. 复合类别支持的多元线性回归遥感影像色彩归一化方法[J]. 地球信息科学学报, 2016, 18(5): 615-621.DOI:10.3724/SP.J.1047.2016.00615

WU Wei,CHENG Xi,GU Guomin. Compound Cluster Center Based Multiple Linear Regression Color Normalization Method for Remote Sensing Image[J]. Journal of Geo-information Science, 2016, 18(5): 615-621.DOI:10.3724/SP.J.1047.2016.00615

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参考文献 20

[1]	Du Y, Teillet P M, Cihlar J.Radiometric normalization of multitemporal high-resolution satellite images with quality control for land cover change detection[J]. Remote Sensing of Environment, 2002,82(1):123-134. doi: 10.1016/S0034-4257(02)00029-9
[2]	Demir B, Bovolo F, Bruzzone L.Detection of land-cover transitions in multitemporal remote sensing images with active-learning-based compound classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012,50(5):1930-1941. doi: 10.1109/TGRS.2011.2168534
[3]	Du Y, Cihlar J, Beaubien J, et al.Quality control for satellite high resolution image mosaics over large areas[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001,39(3):623-634.
[4]	Canty M J.Automatic radiometric normalization of multitemporal satellite imagery[J]. Remote Sensing of Environment, 2004,91(3-4):441-451. doi: 10.1016/S0034-4257(04)00120-8
[5]	Canty M J, Nielsen A A.Automatic radiometric normalization of multitemporal satellite imagery with the iteratively re-weighted MAD transformation[J]. Remote Sensing of Environment, 2008,112:1025-1036. doi: 10.1016/j.rse.2007.07.013
[6]	Zhang L, Wu C, Du B.Automatic radiometric normalization for multitemporal remote sensing imagery with iterative slow feature analysis[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014,52(10):6141-6155. doi: 10.1109/TGRS.2013.2295263
[7]	Chen X, Vierling L, Deering D.A simple and effective radiometric correction method to improve landscape change detection across sensors and across time[J]. Remote Sensing of Environment, 2005,98(1):63-79. doi: 10.1016/j.rse.2005.05.021
[8]	Koukal T, Suppan F, Schneider W.The impact of relative radiometric calibration on the accuracy of KNN-predictions of forest attributes[J]. Remote Sensing of Environment, 2007,110(4):431-437. doi: 10.1016/j.rse.2006.08.016
[9]	Schroeder T A, Cohen W B, Song C, et al.Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in Western Oregon[J]. Remote Sensing of Environment, 2006,103(1):16-26.
[10]	吴炜,骆剑承,李均力,等.面向遥感影像镶嵌的SVR色彩一致性处理[J].中国图象图形学报,2012,17(12):1561-1567. doi: 10.11834/jig.20121215
	[ Wu W, Luo J C, Li J L, et al.Support vector regression color normalization method for image mosaic[J]. Journal of Image and Graphics, 2012,17(12):1561-1567. ] doi: 10.11834/jig.20121215
[11]	Liu S H, Lin C W, Chen Y R, et al.Automatic radiometric normalization with genetic algorithms and a Kriging model[J]. Computers & Geosciences, 2012,43:42-51. doi: 10.1016/j.cageo.2011.12.016
[12]	肖甫,吴慧中,肖亮,等.一种光照鲁棒的图像拼接融合算法[J].中国图象图形学报,2007,12(9):1671-1675. doi: 10.11834/jig.20070929
	[ Xiao F, Wu H Z, Xiao L.An ambient light independent image mosaic algorithm[J]. Journal of Image and Graphics, 2007,12(9):1671-1675. ] doi: 10.11834/jig.20070929
[13]	陈建乐,刘济林,叶建洪,等.多视点视频中基于局部直方图匹配的亮度和色差校正[J].中国图象图形学报,2007,12(11):1992-1999. doi: 10.11834/jig.20071108
	[ Chen J L, Liu J L, Ye H L, et al.Luminance and chrominance correction for multi-view video using overlapped local histogram matching[J]. Journal of Image and Graphics, 2007,12(11):1992-1999. ] doi: 10.11834/jig.20071108
[14]	Gonzalez R C.Digital image processing[M]. New Jersey: Prentice-Hall, 2002.
[15]	章毓晋. 图像工程[M].北京:清华大学出版社,2005.
	[ Zhang Y J.Image engineering[M]. Beijing: Tsinghua Press, 2005. ]
[16]	Nikolova M, Wen Y W, Chan R.Exact histogram specification for digital images using a variational approach[J]. Journal of Mathematical Imaging and Vision, 2012,46(3):309-325. doi: 10.1007/s10851-012-0401-8
[17]	Mignotte M.An energy-based model for the image edge-histogram specification problem[J]. IEEE Transactions on Image Processing, 2012,21(1):379-386. doi: 10.1109/TIP.2011.2159804 pmid: 21693426
[18]	Inamdar S, Bovolo F, Bruzzone L, et al.Multidimensional probability density function matching for preprocessing of multitemporal remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008,46(4):1243-1252. doi: 10.1109/TGRS.2007.912445
[19]	Mas J F.Monitoring land-cover changes: a comparison of change detection techniques[J]. International Journal of Remote Sensing, 1999,20(1):139-152. doi: 10.1080/014311699213659
[20]	Helmer E H, Ruefenacht B.Cloud-free satellite image mosaics with regression trees and histogram matching[J]. Photogrammetric Engineering & Remote Sensing, 2005,71(9):1079-1089. doi: 10.14358/PERS.71.9.1079

		SR			R			C
		B_N	B_R	B_G	B_N	B_R	B_G	B_N	B_R	B_G
试验1	本文方法	129	110	82	7.9	6.9	9.5	0.95	0.91	0.89
试验1	对比方法	109	85	63	10.5	9.6	11.6	0.75	0.77	0.84
试验2	本文方法	160	141	127	11.3	13.1	12.5	0.82	0.85	0.84
试验2	对比方法	128	115	105		14.6	13.5		0.80	0.79

复合类别支持的多元线性回归遥感影像色彩归一化方法

Compound Cluster Center Based Multiple Linear Regression Color Normalization Method for Remote Sensing Image

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