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TitleHaze removal for new generation optical sensors
AuthorHong, G; Zhang, Y
SourceInternational Journal of Remote Sensing vol. 39, no. 5, 2017 p. 1491-1509, https://doi.org/10.1080/01431161.2017.1407048
Year2017
Alt SeriesEarth Sciences Sector, Contribution Series 20160439
PublisherInforma UK Limited
Documentserial
Lang.English
Mediapaper; on-line; digital
File formatpdf; html
Subjectsgeophysics; mathematical and computational geology; remote sensing; satellites; methodology; optical sensors; data processing; haze optimized transform (HOT); radiometric resolution; filters; data corrections; Landsat 8; Worldview-3; QuickBird
Illustrationsflow diagrams; satellite images; schematic representations; graphs; tables
ProgramMethodology, Remote Sensing Science
Released2017 11 29
AbstractHaze 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 ID299881