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
查看参考文献20篇
|
文摘
|
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。 |
|
其他语种文摘
|
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. |
|
来源
|
地球信息科学学报
,2016,18(5):615-621 【核心库】
|
|
关键词
|
色彩归一化
;
复合类别
;
色彩保持
;
多元线性回归
|
|
地址
|
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
|
|
语种
|
中文 |
|
文献类型
|
研究性论文 |
|
ISSN
|
1560-8999 |
|
学科
|
测绘学 |
|
基金
|
国家自然科学基金
;
中国科学院重点部署项目
|
|
文献收藏号
|
CSCD:5694333
|
参考文献 共
20
共1页
|
|
1.
Du Y. Radiometric normalization of multitemporal high-resolution satellite images with quality control for land cover change detection.
Remote Sensing of Environment,2002,82(1):123-134
|
CSCD被引
32
次
|
|
|
|
|
2.
Demir B. Detection of land-cover transitions in multitemporal remote sensing images with active-learning-based compound classification.
IEEE Transactions on Geoscience and Remote Sensing,2012,50(5):1930-1941
|
CSCD被引
7
次
|
|
|
|
|
3.
Du Y. Quality control for satellite high resolution image mosaics over large areas.
IEEE Transactions on Geoscience and Remote Sensing,2001,39(3):623-634
|
CSCD被引
10
次
|
|
|
|
|
4.
Canty M J. Automatic radiometric normalization of multitemporal satellite imagery.
Remote Sensing of Environment,2004,91(3/4):441-451
|
CSCD被引
33
次
|
|
|
|
|
5.
Canty M J. Automatic radiometric normalization of multitemporal satellite imagery with the iteratively re-weighted MAD transformation.
Remote Sensing of Environment,2008,112:1025-1036
|
CSCD被引
40
次
|
|
|
|
|
6.
Zhang L. Automatic radiometric normalization for multitemporal remote sensing imagery with iterative slow feature analysis.
IEEE Transactions on Geoscience and Remote Sensing,2014,52(10):6141-6155
|
CSCD被引
7
次
|
|
|
|
|
7.
Chen X. A simple and effective radiometric correction method to improve landscape change detection across sensors and across time.
Remote Sensing of Environment,2005,98(1):63-79
|
CSCD被引
23
次
|
|
|
|
|
8.
Koukal T. The impact of relative radiometric calibration on the accuracy of KNN-predictions of forest attributes.
Remote Sensing of Environment,2007,110(4):431-437
|
CSCD被引
4
次
|
|
|
|
|
9.
Schroeder T A. Radiometric correction of multi-temporal Landsat data for characterization of early successional forest patterns in Western Oregon.
Remote Sensing of Environment,2006,103(1):16-26
|
CSCD被引
17
次
|
|
|
|
|
10.
吴炜. 面向遥感影像镶嵌的SVR色彩一致性处理.
中国图象图形学报,2012,17(12):1561-1567
|
CSCD被引
3
次
|
|
|
|
|
11.
Liu S H. Automatic radiometric normalization with genetic algorithms and a Kriging model.
Computers & Geosciences,2012,43:42-51
|
CSCD被引
2
次
|
|
|
|
|
12.
肖甫. 一种光照鲁棒的图像拼接融合算法.
中国图象图形学报,2007,12(9):1671-1675
|
CSCD被引
11
次
|
|
|
|
|
13.
陈建乐. 多视点视频中基于局部直方图匹配的亮度和色差校正.
中国图象图形学报,2007,12(11):1992-1999
|
CSCD被引
9
次
|
|
|
|
|
14.
Gonzalez R C.
Digital image processing,2002
|
CSCD被引
127
次
|
|
|
|
|
15.
章毓晋.
图像工程,2005
|
CSCD被引
11
次
|
|
|
|
|
16.
Nikolova M. Exact histogram specification for digital images using a variational approach.
Journal of Mathematical Imaging and Vision,2012,46(3):309-325
|
CSCD被引
1
次
|
|
|
|
|
17.
Mignotte M. An energy-based model for the image edge-histogram specification problem.
IEEE Transactions on Image Processing,2012,21(1):379-386
|
CSCD被引
1
次
|
|
|
|
|
18.
Inamdar S. Multidimensional probability density function matching for preprocessing of multitemporal remote sensing images.
IEEE Transactions on Geoscience and Remote Sensing,2008,46(4):1243-1252
|
CSCD被引
2
次
|
|
|
|
|
19.
Mas J F. Monitoring land-cover changes: a comparison of change detection techniques.
International Journal of Remote Sensing,1999,20(1):139-152
|
CSCD被引
53
次
|
|
|
|
|
20.
Helmer E H. Cloud-free satellite image mosaics with regression trees and histogram matching.
Photogrammetric Engineering & Remote Sensing,2005,71(9):1079-1089
|
CSCD被引
15
次
|
|
|
|
|
了解气象卫星更多信息，请访问