Journal Title : International Journal of Modern Trends in Engineering and Science
Paper Title : HAZE AND CIRRUS REMOVAL FOR REMOTE SENSING IMAGE BY HAZE THICKNESS MAP ESTIMATION
Volume 03 Issue 08 2016
ISSN no: 2348-3121
Page no: 94-96
Abstract – Multispectral satellite images are often affected by haze and/or cirrus. A previous paper conferred a haze removal method that calculates a haze thickness based on a local search of dark objects. The haze-free signal is restored by subtracting the HTM from the affected image. The HTM method is substantially improved by employing the 1.38-μm cirrus band. The top-of-troposphere reflectance cirrus band is used as an additional source of message. The process restores the information in highly inhomogeneous surfaces extenuated by a low-altitude haze and high-altitude cirrus, improving the clarification of the scene information while preserving the shape of the spectral signatures. A novel combined haze and cirrus removal that uses visible bands and a cirrus band to calculate the HTM.There are three different approaches to solve this problem are method for dehazing ,method for cirrus removal,and developed haze and cirrus combined approach .The advantage of this method over state of art cirrus algorithm is independent of cirrus correlation parameter estimation .The new method perform better than the previous haze removal and also better than the current state of art cirrus removal.
Keywords— Image processing, remote sensing image,haze detection ,cirrus detection, Haze thickness map estimation
- H. Chepfer et al., “Cirrus cloud properties derived from POLDER-1/ ADEOS polarized radiances: First validation using a ground-based lidar network,” J. Appl. Meteorol., vol. 39, no. 2, pp. 154–168, Feb. 2000.
- Y. Zhang and B. Guindon, “Quantitative assessment of a haze suppres-sion methodology for satellite imagery: Effect on land cover classifi-cation performance,”IEEE Trans. Geosci. Remote Sci., vol. 41, no. 5, pp. 1082–1089, May 2003.
- C.Liu,J.Hu,Y. Lin,S.Wu,and W.Huang, “Haze detection, perfection and removal for high spatial resolution satellite imagery,”Int. J. Remote Sens., vol. 32, no. 23, pp. 8685–8697, Dec. 2011.
- J. Long, Z. Shi, W. Tang, and C. Zhang, “Single remote sensing image dehazing,”IEEE Geosci. Remote Sci. Lett., vol. 11, no. 1, pp. 59–63,Jan. 2014.
- A. Makarau, R. Richter, R. Müller, and P Reinartz, “Haze detection and removal in remotely sensed multispectral imagery,”IEEE Trans. Geosci. Remote Sci., vol. 52, no. 9, pp. 5895–5905, Sep. 2014.
- B.-C. Gao, P. Yang, W. Han, R.-R. Li, and W. J. Wiscombe, “An algorithm using visible and 1.38μm channels to retrieve cirrus cloud reflectances from aircraft and satellite data,” IEEE Trans. Geosci. Remote Sci., vol. 40, no. 8, pp. 1659–1668, Aug. 2002.
- B.-C. Gao, K. Meyer, and P. Yang, “A new concept on remote sensing of cirrus optical depth and effective ice particle size using strong water vapour absorption channels near 1.38 and 1.88μm,”IEEE Trans. Geosci. Remote Sci., vol. 42, no. 9, pp. 1891–1899, Sep. 2004.
- R. Richter, X. Wang, M. Bachmann, and D. Schläpfer, “Correction of cirrus effects in Sentinel-2 type of imagery,”Int. J. Remote Sens., vol. 32, no. 10, pp. 2931–2941, May 2011.
- B.-C. Gao and Y. J. Kaufman, “Selection of 1.375μm MODIS channel for remote sensing of cirrus clouds and stratospheric aerosols from space,” J. Atmos. Sci., vol. 52, no. 23, pp. 4231–4237, Dec. 1995.
- G. Vane et al., “The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS),” Remote Sens. Environ., vol. 44, no. 2/3, pp. 127–143, May/Jun. 1993.
- ATCOR, Atmospheric/Topographic Correction for Satellite Imagery, ReSe Applications Schläpfer, Wil,Switzerland, 2011.
- P. Chavez, An improved dark-object subtraction technique for at-mospheric scattering correction of multispectral data,Remote Sens. Environ., vol. 24, no. 3, pp. 459–479, Apr. 1988.
- M. Xu, X. Jia, and M. Pickering, “Automatic cloud removal for Landsat 8 OLI images using cirrus band,” inProc. IEEE Int. Geosci. Remote Sens. Symp., 2014, pp.2511–2514.
- C. A. Z. Barcelos and V. B. Pires, ‘‘An automatic based nonlinear diffusion equations scheme for skin lesion segmentation,’’ Appl. Math. Comput., vol. 215, no. 1, pp. 251–261, 2009.