| Title | Haze removal for new generation optical sensors |
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| Author | Hong, G; Zhang, Y |
| Source | International Journal of Remote Sensing vol. 39, no. 5, 2017 p. 1491-1509, https://doi.org/10.1080/01431161.2017.1407048 |
| Image |  |
| Year | 2017 |
| Alt Series | Earth Sciences Sector, Contribution Series 20160439 |
| Publisher | Informa UK Limited |
| Document | serial |
| Lang. | English |
| Media | paper; on-line; digital |
| File format | pdf; html |
| Subjects | geophysics; mathematical and computational geology; remote sensing; satellites; Methodology; Data processing |
| Illustrations | flow diagrams; satellite images; schematic representations; graphs; tables |
| Program | Remote Sensing Science |
| Released | 2017 11 29 |
| Abstract | Haze and cloud contamination is a common problem in optical remote-sensing imagery, as it can lead to the inaccurate estimation of physical properties of the surface derived from remote sensing and
reduced accuracy of land cover classification and change detection. Haze optimized transform (HOT) is a methodology applicable to radiometric compensation of additive haze effects in visible bands that exhibits a spatially complex distribution over
an image. The generic approach of HOT allows for the use of older satellite imaging sensors that include at least two visible bands (e.g. Landsat Thematic Mapper (TM) and Landsat Multispectral Scanner (MSS) sensors). This study proposes modifications
to extend HOT applicability to new sensors. The improvements and extended functionality adapt the method to the higher radiometric resolution specifications of newer generation sensors and use percentile-based minimum in the correction procedure to
avoid causing fake minimum. Alternative filters are also evaluated to smooth raw HOT output and the cloud mask is generated as an additional output. A Landsat 8 scene of Los Angeles is used to demonstrate the improved methodology. The methodology is
applicable to sensors such as QuickBird, Worldview 2/3. More than 20 additional scenes were used to evaluate the effectiveness of the methodology. |
| Summary | (Plain Language Summary, not published)
Haze and cloud are two barriers to interpreting and analyzing optical remote sensing imagery. In 2002, CCRS developed an image-based correction
procedure to remove thin cloud/haze in optical images. The method, which has been widely used, is applicable to older satellite optical sensors (such as Landsat MSS and TM). This study improved the method and expanded its applicability to new
generation, higher-resolution sensors such as Landsat 8, QuickBird, and Worldview 2/3. A Landsat 8 scene of Los Angeles was used to demonstrate the improved methodology, and more than twenty additional scenes were used to evaluate the effectiveness
of the methodology. |
| GEOSCAN ID | 299881 |
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