Haze removal for new generation optical sensors

References

• Asmala, A., and Q. Shaun. 2015. “The Effects of Haze on the Accuracy of Satellite Land Cover Classification.” Applied Mathematical Sciences 9 (49): 2433–2443. doi:10.12988/ams.2015.52157.
   Google Scholar
• Carlotto, M. J. 1999. “Reducing the Effects of Space-Varying, Wavelength Dependent Scattering in Multispectral Imagery.” International Journal of Remote Sensing 20 (17): 3333–3344. doi:10.1080/014311699211363.
   Web of Science ®Google Scholar
• Chavez, P. S. 1988. “An Improved Dark-Object Subtraction Technique for Atmospheric Scattering Correction of Multispectral Data.” Remote Sensing of Environment 24 (3): 459–479. doi:10.1016/0034-4257(88)90019-3.
   Web of Science ®Google Scholar
• Crist, E. P. 1984. “A Spectral Haze Diagnostic Feature for Normalizing Landsat Thematic Mapper Data.” In Proceedings of the 18th International Symposium on Remote Sensing of Environment (Paris), 735–744. Ann Harbor, Michigan: ERIM.
   Google Scholar
• Du, Y., B. Guindon, and J. Cihlar. 2002. “Haze Detection and Removal in High Resolution Satellite Images with Wavelet Analysis.” IEEE Transactions on Geoscience and Remote Sensing 40 (1): 210–217. doi:10.1109/36.981363.
   Web of Science ®Google Scholar
• EROS. 2016. Landsat 8 (L8) Data Users Handbook. Accessed 24 June 2016. http://landsat.usgs.gov/documents/Landsat8DataUsersHandbook.pdf
   Google Scholar
• Feyisa, G. L., H. Meilby, R. Fensholt, and S. R. Proud. 2014. “Automated Water Extraction Index: A New Technique for Surface Water Mapping Using Landsat Imagery.” Remote Sensing of Environment 140: 23–35. doi:10.1016/j.rse.2013.08.029.
   Web of Science ®Google Scholar
• García‐López, S., J. Delgado, J. Cardenal, and J. Caracuel. 2005. “Using PCA Transformation to Remove the Tenuous Cloudiness Effect in Multispectral Satellite Sensor Images.” International Journal of Remote Sensing 26 (1): 209–216. doi:10.1080/0143116042000274003.
   Web of Science ®Google Scholar
• Guindon, B., and Y. Zhang. 2002. Robust Haze Reduction: An Integral Processing Component in Satellite-Based Land Cover Mapping. In Proceeding of the Joint International Symposium on Geospatial Theory, Processing and Applications Ottawa, ON, Canada, July 8–12.
   Google Scholar
• He, X. Y., J. B. Hu, W. Chen, and X. Y. Li. 2010. “Haze Removal Based on Advanced Haze-Optimized Transformation (AHOT) for Multispectral Imagery.” International Journal of Remote Sensing 31 (20): 5331–5348. doi:10.1080/01431160903369600.
   Web of Science ®Google Scholar
• Huang, C., S. N. Goward, J. G. Masek, N. Thomas, Z. Zhu, and J. E. Vogelmann. 2010. “An Automated Approach for Reconstructing Recent Forest Disturbance History Using Dense Landsat Time Series Stacks.” Remote Sensing of Environment 114: 183–198. doi:10.1016/j.rse.2009.08.017.
   Web of Science ®Google Scholar
• Irons, J. R., J. L. Dwyer, and J. A. Barsi. 2012. “The Next Landsat Satellite: The Landsat Data Continuity Mission.” Remote Sensing of Environment 122: 11–21. doi:10.1016/j.rse.2011.08.026.
   Web of Science ®Google Scholar
• Ji, C. Y. 2008. “Haze Reduction from the Visible Bands of Landsat TM and ETM+ Images over a Shallow Water Reef Environment.” Remote Sensing of Environment 112: 1773–1783. doi:10.1016/j.rse.2007.09.006.
   Web of Science ®Google Scholar
• Krieger, F., W. Malila, R. Nalepka, and W. Richerdson. 1969. “Preprocessing Transformations and Their Effects on Multispectral Recognition.” In Proceedings of the Sixth International Symposium on Remote Sensing of Environment, 97–131. Ann Arbor, MI: University of Michigan.
   Google Scholar
• Lavreau, J. 1991. “De-Hazing Landsat Thematic Mapper Images.” Photogrammetric Engineering and Remote Sensing 57 (10): 1297–1302.
   Web of Science ®Google Scholar
• Liang, S. L., H. L. Fang, and M. Z. Chen. 2001. “Atmospheric Correction of Landsat ETM Land Surface Imagery – Part I: Methods.” IEEE Transactions on Geoscience and Remote Sensing 39: 2490–2498. doi:10.1109/36.964986.
   Web of Science ®Google Scholar
• Liang, S. L., H. L. Fang, J. T. Moriseizette, M. Z. Chen, C. J. Shuey, C. L. Walthall, and C. S. T. Daughtry. 2002. “Atmospheric Correction of Landsat ETM Plus Land Surface Imagery – Part II: Validation and Applications.” IEEE Transactions on Geoscience and Remote Sensing 40: 2736–2746. doi:10.1109/TGRS.2002.807579.
   Web of Science ®Google Scholar
• Liu, C., J. Hu, Y. Lin, S. Wu, and W. Huang. 2011. “Haze Detection, Perfection and Removal for High Spatial Resolution Satellite Imagery.” International Journal of Remote Sensing 32 (23): 8685–8697. doi:10.1080/01431161.2010.547884.
   Web of Science ®Google Scholar
• Luo, Y., A. P. Trishchenko, and K. V. Khlopenkov. 2008. “Developing Clear-Sky, Cloud and Cloud Shadow Mask for Producing Clear-Sky Composites at 250-Meter Spatial Resolution for the Seven MODIS Land Bands over Canada and North America.” Remote Sensing of Environment 112: 4167–4185. doi:10.1016/j.rse.2008.06.010.
   Web of Science ®Google Scholar
• McFeeters, S. K. 1996. “The Use of the Normalized Difference Water Index (NDWI) in the Delineation of Open Water Features.” International Journal of Remote Sensing 17 (7): 1425–1432. doi:10.1080/01431169608948714.
   Web of Science ®Google Scholar
• Moro, G. D., and L. Halounova. 2007. “Haze Removal for High‐Resolution Satellite Data: A Case Study.” International Journal of Remote Sensing 28 (10): 2187–2205. doi:10.1080/01431160600928559.
   Web of Science ®Google Scholar
• Richter, R. 1996. “A Spatially Adaptive Fast Atmospheric Correction Algorithm.” International Journal of Remote Sensing 17 (6): 1201–1214. doi:10.1080/01431169608949077.
   Web of Science ®Google Scholar
• Richter, R., and D. Schlapfer. 2012. “Atmospheric/Topographic Correction for Airborne Imagery: ATCOR-4 User Guide.” DLR IB, 565-02/12. http://www.dlr.de/eoc/Portaldata/60/Resources/dokumente/5_tech_mod/atcor4_manual_2012.pdf
   Google Scholar
• Rokni, K., A. Ahmad, A. Selamat, and S. Hazini. 2014. “Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery.” Remote Sensing 6 (5): 4173–4189. doi:10.3390/rs6054173.
   Web of Science ®Google Scholar
• Shen, H., H. Li, Y. Qian, L. Zhang, and Q. Yuan. 2014. “An Effective Thin Cloud Removal Procedure for Visible Remote Sensing Images.” ISPRS Journal of Photogrammetry and Remote Sensing 96: 224–235. doi:10.1016/j.isprsjprs.2014.06.011.
   Web of Science ®Google Scholar
• Shen, Y., Y. Wang, H. Lv, and H. Li. 2015. “Removal of Thin Clouds Using Cirrus and QA Bands of Landsat-8.” Photogrammetric Engineering & Remote Sensing 81 (9): 721–731. doi:10.14358/PERS.81.9.721.
   Web of Science ®Google Scholar
• Teillet, P. M., and G. Fedosejevs. 1995. “On the Dark Target Approach to Atmospheric Correction of Remotely Sensed Data.” Canadian Journal of Remote Sensing 21: 374–387. doi:10.1080/07038992.1995.10855161.
   Google Scholar
• Teillet, P. M., N. T. O’neill, A. Kalinauskas, D. Sturgeon, and G. Fedosejevs. 1987. “A Dynamic Regression Algorithm for Incorporating Atmospheric Models into Image Correction Procedures.” In Proceedings of IGARSS’87- International Geoscience and Remote Sensing Symposium, 913–918, Ann Arbor, MI, May 18-21.
   Google Scholar
• Tupas, M. E. 2015. “Haze Optimized Transform on LANDSAT 8 Imagery for Thin Cloud Detection and Removal.” Philippine Engineering Journal 36 (2): 69–86.
   Google Scholar
• Xiao, Y., Z. Ouyang, Z. Zhang, and C. Xian. 2017. “A Comparison of Haze Removal Algorithms and Their Impacts on Classification Accuracy for Landsat Imagery.” Boletim De Ciências Geodésicas 23 (1): 55–71. doi:10.1590/s1982-21702017000100004.
   Web of Science ®Google Scholar
• Xu, M., M. Pickering, A. J. Plaza, and X. Jia. 2016. “Thin Cloud Removal Based on Signal Transmission Principles and Spectral Mixture Analysis.” IEEE Transactions on Geoscience and Remote Sensing 54 (3): 1659–1669. doi:10.1109/TGRS.2015.2486780.
   Web of Science ®Google Scholar
• Zhang, Y., and B. Guindon. 2003. “Quantitative Assessment of a Haze Suppression Methodology for Satellite Imagery: Effect on Land Cover Classification Performance.” IEEE Transactions on Geoscience and Remote Sensing 41 (5): 1082–1089. doi:10.1109/TGRS.2003.811817.
   Web of Science ®Google Scholar
• Zhang, Y., B. Guindon, and J. Cihlar. 2002. “An Image Transform to Characterize and Compensate for Spatial Variations in Thin Cloud Contamination of Landsat Images.” Remote Sensing of Environment 82: 173–187. doi:10.1016/S0034-4257(02)00034-2.
   Web of Science ®Google Scholar
• Zhang, Y., B. Guindon, and X. Li. 2014. “A Robust Approach for Object-Based Detection and Radiometric Characterization of Cloud Shadow Using Haze Optimized Transformation.” IEEE Transactions on Geoscience and Remote Sensing 52 (9): 5540–5547. doi:10.1109/TGRS.2013.2290237.
   Web of Science ®Google Scholar
• Zhu, Z., and C. E. Woodcock. 2012. “Object-Based Cloud and Cloud Shadow Detection in Landsat Imagery.” Remote Sensing of Environment 118: 83–94. doi:10.1016/j.rse.2011.10.028.
   Web of Science ®Google Scholar