| Title | Haze removal based on a fully automated and improved haze optimized transformation for Landsat imagery over land |
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| Author | Sun, L; Latifovic, R; Pouliot, D |
| Source | Remote Sensing vol. 9, no. 10, 972, 2017 p. 1-21, https://doi.org/10.3390/rs9100972  Open Access |
| Image |  |
| Year | 2017 |
| Alt Series | Natural Resources Canada, Contribution Series 20170387 |
| Publisher | MDPI AG |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf; html |
| Subjects | geophysics; remote sensing |
| Program | Remote Sensing Science |
| Released | 2017 09 21 |
| Abstract | Optical satellite imagery is often contaminated by the persistent presence of clouds and atmospheric haze. Without an effective method for removing this contamination, most optical remote sensing
applications are less reliable. In this research, a methodology has been developed to fully automate and improve the Haze Optimized Transformation (HOT)-based haze removal. The method is referred to as AutoHOT and characterized with three notable
features: a fully automated HOT process, a novel HOT image post-processing tool and a class-based HOT radiometric adjustment method. The performances of AutoHOT in haze detection and compensation were evaluated through three experiments with one
Landsat-5 TM, one Landsat-7 ETM+ and eight Landsat-8 OLI scenes that encompass diverse landscapes and atmospheric haze conditions. The first experiment confirms that AutoHOT is robust and effective for haze detection. The average overall, user's and
producer's accuracies of AutoHOT in haze detection can reach 96.4%, 97.6% and 97.5%, respectively. The second and third experiments demonstrate that AutoHOT can not only accurately characterize the haze intensities but also improve dehazed results,
especially for brighter targets, compared to traditional HOT radiometric adjustment. |
| Summary | (Plain Language Summary, not published)
Optical satellite imagery is often contaminated by the persistent presence of clouds and atmospheric haze. Without an effective method for removing this
contamination, the accuracy of information extracted from these images is less reliable. In this research, a methodology has been developed to fully automate and improve an existing method (the Haze Optimized Transformation or HOT) to remove haze
from optical images. The performance of the new method, referred to as AutoHOT, was evaluated through three experiments using images that encompass diverse landscapes and atmospheric haze conditions. The results of the experiments confirm that the
developed AutoHOT methodology is a more robust and effective approach for haze detection and removal, thus resulting in improved information extraction from optical images. |
| GEOSCAN ID | 311415 |
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