作者:Cao, BA (Cao, Biao)[ 1,2 ] ; Du, YM (Du, Yongming)[ 1,2 ] ; Xu, DQ (Xu, Daqi)[ 2 ] ; Li, H (Li, Hua)[ 1,2 ] ; Liu, QH (Liu, Qinhuo)[ 1,2 ]

OPTIK

卷: 126  期: 23  页: 4723-4730

DOI: 10.1016/j.ijleo.2015.08.079

出版年: 2015

摘要

Striping noise is a well-known phenomenon that arises in most multi-detector optical imaging instruments. Such noise affects both visual interpretation and quantitative analysis. Therefore, destriping is an essential step before absolute calibration and image interpretation. Histogram matching is one of the most popular algorithms used to reduce striping. The assumption underlying histogram matching is that each detector has the same gray level distribution. This assumption is easily satisfied when the image is sufficiently large, but it often cannot be satisfied for small images. An improved histogram matching algorithm based on sliding windows is proposed in this paper. The algorithm presupposes that the gray level distribution of each column (taking the vertical striping noise as an example) is similar to the gray level distribution of the column-centered local area. The size of the local area is determined by a histogram growing algorithm. Compact High Resolution Imaging Spectrometer (CHRIS), Moderate Resolution Imaging Spectroradiometer (MODIS) and Hyperspectral Imager (HSI) images were used to test the new and traditional algorithms. These destriping results were compared using improvement factors, inverse coefficients of variation and mean profiles. The results of the comparison indicate that the improved histogram matching algorithm has obvious advantages over traditional method. (C) 2015 Published by Elsevier GmbH.

通讯作者地址: Du, YM (通讯作者)

       CAS Olymp S&T Pk,20 Da Tun Rd,POB 9718, Beijing 100101, Peoples R China.

地址:

       [ 1 ] Inst Remote Sensing & Digital Earth Chinese Acad, State Key Lab Remote Sensing Sci, Beijing, Peoples R China

       [ 2 ] Joint Ctr Global Change Studies, Beijing 100875, Peoples R China