An improved histogram matching algorithm for the removal of striping noise in optical remote sensing imagery

Abstract 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.

[1]  D. Helder,et al.  A technique for the reduction of banding in Landsat Thematic Mapper Images , 1992 .

[2]  Fuan Tsai,et al.  Striping Noise Detection and Correction of Remote Sensing Images , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Nianzeng Che,et al.  A new method for retrieving band 6 of aqua MODIS , 2006, IEEE Geoscience and Remote Sensing Letters.

[4]  Limin Yang,et al.  Oblique striping removal in remote sensing imagery based on wavelet transform , 2006 .

[5]  Tim R. McVicar,et al.  Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes , 2003, IEEE Trans. Geosci. Remote. Sens..

[6]  Hervé Carfantan,et al.  Statistical Linear Destriping of Satellite-Based Pushbroom-Type Images , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Angela Lausch,et al.  Reduction of Radiometric Miscalibration—Applications to Pushbroom Sensors , 2011, Sensors.

[8]  Amr Abd-Elrahman,et al.  De-striping hyperspectral imagery using wavelet transform and adaptive frequency domain filtering , 2011 .

[9]  J. J. Simpson,et al.  Improved Finite Impulse Response Filters for Enhanced Destriping of Geostationary Satellite Data , 1998 .

[10]  Wataru Takeuchi,et al.  Stripe Noise Reduction in MODIS Data by Combining Histogram Matching With Facet Filter , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Leonid P. Yaroslavsky.  Theoretical Foundations of Digital Imaging Using MATLAB , 2012 .

[12]  F. L. Gadallah,et al.  Destriping multisensor imagery with moment matching , 2000 .

[13]  James J. Simpson,et al.  Reduction of noise in AVHRR channel 3 data with minimum distortion , 1994, IEEE Trans. Geosci. Remote. Sens..

[14]  Robert J. Woodham,et al.  Destriping LANDSAT MSS images by histogram modification , 1979 .

[15]  M. Wegener.  Destriping multiple sensor imagery by improved histogram matching , 1990 .

[16]  D. J. Poros,et al.  Methods for destriping Landsat Thematic Mapper images - A feasibility study for an online destriping process in the Thematic Mapper Image Processing System (TIPS) , 1985 .

[17]  Jeng-Jong Pan,et al.  Destriping of Landsat MSS images by filtering techniques , 1992 .

[18]  Robert A. Neville,et al.  Automatic destriping of Hyperion imagery based on spectral moment matching , 2008 .

[19]  Martin Vetterli,et al.  Adaptive wavelet thresholding for image denoising and compression , 2000, IEEE Trans. Image Process..

[20]  Jorge Torres,et al.  Wavelet analysis for the elimination of striping noise in satellite images , 2001 .

[21]  R. Crippen.  A simple spatial filtering routine for the cosmetic removal of scan-line noise from Landsat TM P-tape imagery , 1989 .

[22]  Jian Guo Liu,et al.  FFT Selective and Adaptive Filtering for Removal of Systematic Noise in ETM+ Imageodesy Images , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[23]  James M. White,et al.  Landsat Data Destriping Using Power Spectral Filtering , 1988 .

[24]  Liangpei Zhang,et al.  A MAP-Based Algorithm for Destriping and Inpainting of Remotely Sensed Images , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[25]  V. Algazi,et al.  Radiometric equalization of nonperiodic striping in satellite data , 1981 .

[26]  Huadong Guo,et al.  Destriping CMODIS data by power filtering , 2003, IEEE Trans. Geosci. Remote. Sens..

[27]  Marco Diani,et al.  Striping removal in MOS-B data , 2000, IEEE Trans. Geosci. Remote. Sens..

[28]  Leonid Yaroslavsky.  Digital Holography and Digital Image Processing , 2004, Springer US.

[29]  Saïd Ladjal,et al.  Toward Optimal Destriping of MODIS Data Using a Unidirectional Variational Model , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[30]  Roberto Episcopo,et al.  Destriping MODIS Data Using Overlapping Field-of-View Method , 2009, IEEE Transactions on Geoscience and Remote Sensing.