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. 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 Wei1,*(), CHENG Xi2, GU Guomin1   

  1. 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 E-mail:wuwei@zjut.edu.cn

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