Haze detection and removal in remotely sensed multispectral imagery
IEEE Transactions on Geoscience and Remote Sensing, 2014•ieeexplore.ieee.org
Haze degrades optical data and reduces the accuracy of data interpretation. Haze detection
and removal is a challenging and important task for optical multispectral data correction.
This paper presents an empirical and automatic method for inhomogeneous haze detection
and removal in medium-and high-resolution satellite optical multispectral images. The dark-
object subtraction method is further developed to calculate a haze thickness map, allowing a
spectrally consistent haze removal on calibrated and uncalibrated satellite multispectral …
and removal is a challenging and important task for optical multispectral data correction.
This paper presents an empirical and automatic method for inhomogeneous haze detection
and removal in medium-and high-resolution satellite optical multispectral images. The dark-
object subtraction method is further developed to calculate a haze thickness map, allowing a
spectrally consistent haze removal on calibrated and uncalibrated satellite multispectral …
Haze degrades optical data and reduces the accuracy of data interpretation. Haze detection and removal is a challenging and important task for optical multispectral data correction. This paper presents an empirical and automatic method for inhomogeneous haze detection and removal in medium- and high-resolution satellite optical multispectral images. The dark-object subtraction method is further developed to calculate a haze thickness map, allowing a spectrally consistent haze removal on calibrated and uncalibrated satellite multispectral data. Rare scenes with a uniform and highly reflecting landcover result in limitations of the method. Evaluation on hazy multispectral data (Landsat 8 OLI and WorldView-2) and a comparison to haze-free reference data illustrate the spectral consistency after haze removal.
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