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Proceedings Paper

Space-borne hyperspectral remote sensing imagery noise eliminating based on CFFT self-adapted by optimal SNR
Author(s): Qingjie Liu; Qizhong Lin; Liming Wang; Qinjun Wang; Fengxian Miao
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Paper Abstract

Space-borne hyperspectral remote sensing imagery, supplying both spatial and spectral information for quantitative remote sensing monitoring, is easily polluted by noises from atmosphere, terrain etc. Based on spectral continuum removing and recovering, traditional fast Fourier Transform (FFT) was extended to Continuum Fast Fourier Transform (CFFT) to separate noise from target information in frequency domain (FD). Thus, low-pass filter for reserving useful information was designed for eliminating noise, with its cut-off frequency selected self-adaptively by optimal signal-tonoise ratio (SNR). Hyperion hyperspectral imageries of Beijing and Xinjiang China were singled out for noise removing to validate the filtering ability of the Continuum Fast Fourier Transform self-adapted by Optimal Signal-noise Ratio(CFFTOSNR) method with qualitative description and quantificational indexs, including mean, variance, entropy, definition and SNR etc. Experiment result shows that CFFTOSNR does well in reducing the gauss white noises in spectral domain and stripe and band-subtracting noise in spatial domain respectively, while the quantificational indexs of filtered imagery are all improved, with entropy of post-processed image obviously increased by 5 db.

Paper Details

Date Published: 16 August 2011
PDF: 8 pages
Proc. SPIE 8203, Remote Sensing of the Environment: The 17th China Conference on Remote Sensing, 82030N (16 August 2011); doi: 10.1117/12.910400
Show Author Affiliations
Qingjie Liu, Ctr. for Earth Observation and Digital Earth (China)
Key Lab. of Digital Earth (China)
Qizhong Lin, Ctr. for Earth Observation and Digital Earth (China)
Key Lab. of Digital Earth (China)
Liming Wang, Ctr. for Earth Observation and Digital Earth (China)
Key Lab. of Digital Earth (China)
Qinjun Wang, Ctr. for Earth Observation and Digital Earth (China)
Key Lab. of Digital Earth (China)
Fengxian Miao, STATE Grid AC Engineering Construction Co. (China)


Published in SPIE Proceedings Vol. 8203:
Remote Sensing of the Environment: The 17th China Conference on Remote Sensing
Qingxi Tong; Xingfa Gu; Boqin Zhu, Editor(s)

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