秦邦勇等:A generally applicable noise-estimating method for remote sensing images
被阅读 1211 次
2014-08-22

A generally applicable noise-estimating method for remote sensing images
作者:Qin, BY (Qin, Bangyong)[ 1,2 ] ; Hong, B (Hong, Bo)[ 1 ] ; Zhang, Z (Zhang, Zhi)[ 1 ] ; Yang, XF (Yang, Xiaofeng)[ 1 ] ; Li, ZW (Li, Ziwei)[ 1 ]
REMOTE SENSING LETTERS
卷: 5  期: 5  页: 481-490
DOI: 10.1080/2150704X.2014.923126
出版年: 2014

摘要
Local mean and local standard deviations (LMLSD), which is one of the most widely used methods for estimating noise in remote sensing images, is suitable only for the images with many homogeneous regions. For those composed of heterogeneous features and textures, it may cause overestimation of noise. Edge-extracted local standard deviations (EELSD) method performs better than LMLSD in most instances, but it still cannot work out the accurate noise estimation in most heterogeneous images. Spectral and spatial de-correlation (SSDC) is an effective noise-estimation method for hyperspectral images. However, it cannot be applied to single-band or multispectral images because of the use of pixel spectral information in the calculation process. In this article, a new noise-estimating method for remote sensing images, which is based on the principle of LMLSD and has made improvements in three aspects, is proposed. The new method has been tested with several Airborne Visible Infrared Imaging Spectrometer images with different degrees of uniformity. Compared with LMLSD and EELSD, the results of the improved method are more accurate, stable, and applicable in terms of complex land cover types. Furthermore, in contrast to SSDC, this method is suitable not only for hyperspectral images but also for single-band and multispectral images.

通讯作者地址: Qin, BY (通讯作者)
Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing, Peoples R China.
地址:
[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing, Peoples R China
[ 2 ] Univ Chinese Acad Sci, Coll Resources & Environm, Beijing, Peoples R China