Optical Remote Sensing Image defogging algorithm Based on dcm-htm
Abstract
focusing on fog detection and correction of satellite remote sensing image, a defogging algorithm dcm-htm
for R Emote sensing image is proposed by the combination of Dark channel map (DCM) and haze thickness map
(HTM). Based on the principle of dark channel mean-shift filter and automatic histogram threshold are to used detect
area t the same time, the HTM is obtained by nonoverlapping window. At last, the correction of fog is produced in the
fog area. The results show that the algorithm can accurately detect the fog area, and the areas affected by the haze
can be effecti Vely corrected in the premise of image normal areas not affected. The results are improved greatly
compared with the fog correction of the whole image.
References
Zhou Liya. Research on the information compensation technology of visible light remote sensing images by cloud
interference
[D]. Zhengzhou : University of Information Engineering , 2011:16-24. ZHOU L Y. Information compensation theory of Visible-spectrum cloudy remote sensing imagery [D]
Zhen Gzhou :information Engineering University, : 16-24. 2. Bi Jian , Ge Jing , Liquan , , and so on . Research on a single image-Fog method J. Air Force Journal of
Engineering University : Natural Science Edition , 2013 (6) : 46-53. BI D y, GE y, LI Q H, et al. A defogging Methods with single image ^. Journal of Air Force Engineering
u-niversity: Natural Science Edition, 2013 6 :46-53. 3. Narasimhan s G, NAYAR s K. Vision and the atmosphere
J. International Journal of Computer Vision, 2002, 3 (): 233-254 . 4. Narasimhan s G, NAYAR s K. contrast restoration of weather
| Remote Sensing
degraded images J]. IEEE Transactions on pattern analysis & Machine Intelligence 2003,25 ( 6 :713-724. 5. He K, SUN J, TANG x. Single image haze removal using dark
Channel Prior J]. IEEE Conference on Computer Vision & patter recognition, 2011,33 (12) /b20>:2341-2353. 6. Zhong Zhongming, Chongguang . How to remove thin clouds from remote sensing images J] . Environment
Remote
Sense , 1996,11 (3): 195-199. ZHAO Z M, ZHU C G . Method for removing thin cloud in remote sensing image J] Journal of remote
sensing, b13>1996 (3): 195-199. Land E H. The Retinex theory of color vision J]. Scientific American, 1977,237 (6): 108-129. 8. Zhang , Yang Anhong , Li Yuntao , , and so on . Island Area Image Mist processing model and solid now J. Journal
of Surveying and Mapping science and technology , 2010,27 (4): 266-269. He K, SUN J, TANG X. guided image filtering J] IEEE transactions on pattern analysis & Machine Intelligence, 2013,35 (6): 1397-1409. 10. LAN X, ZHANG L, SHEN H, et al. Single Image haze removal considering sensor blur and noise J] Eurasip
Journal o N Advances in Signal Processing, 2013 (1) : 1-13. 11. He R, WANG Z, FAN Y, et al. Multiple scattering model based single image dehazing [C] // 8th IEEE Conference
on Industrial Electronics and Applications (ICIEA). Melbourne, VIC, 2013:733-737. 12. GUO F, TANG J, CAI Z X. Image dehazing based on haziness analysis J. International Journal of
automation & Computing,2014.11 (1) : 78-86. 13. LONG J, SHI Z, TANG W, et al. Single Remote sensing image dehazing J]. IEEE Geoscience & Remote Sensing
letters, 2014.11 (1) :59-63. 14. WU X, Yang W, LI G. Thin cloud removal of ZY-3 image based on improved homomorphism filtering method
C]/21st I EEE International Conference on Geoinformatics. Kaifeng, 2013,: 1-4. 15. HU C, SHAN X, ZHANG Z. dehazing algorithm ' s high performance and parallel computing for GF-1 satellite C]
/IEEE International Conference on Data Science and Advanced Analytics (DSAA). Shanghai, 2014,:374-378. 16. Reichenbach S E. Automated cloud cover assessment for Landsat TM images J]. Proceedings of Spie-the
International Society for Optical Engineering, 1996,2819: 170-179. 17. Scaramuzza P L, BOUCHARD M, DWYER J. Development of the Landsat data continuity mission Cloud-cover
Assessme NT Algorithms J]. IEEE Transactions on Geoscience &Remote Sensing, 2012,50 (4)
b13>: 1140-1154. 18. HU J B, LIU C B, WANG Z Y, et al improvement on the Three-step haze removal technique with the aid of one
clear Image partly overlapped C]/18th IEEE International Conference on Geoinformatics. Beijing, our,: 1-4. 19. Makarau A, RICHTER R, Mulle R, et al Haze detection and removal in remotely sensed multispectral J]. I EEE
Transactions on Geoscience & Remote Sensing, 2014, 9 b13>:5895-5905. 20. comaniciu D, MEER P. Mean shift: A Robust Approach toward feature analysis J]. IEEE Transactions on pattern
analysis & Machine Intelligence, 2002, 24 ( 5 :603-619. 21. CHAVEZ P S. An improved dark-object subtraction technique for atmospheric scattering correction of
multispectral data J]. Remote Sensing of Environment, 1988, :459-479. 22. ZHANG L , Yang A H, LI X T, et al . Model and realization for island area images dehazing J] Journal of
geomatics Science and Technolo GY, 4 () : 266-269.
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