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23 October 2014 Spectrally consistent haze removal in multispectral data
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The presence of haze reduces the accuracy of optical data interpretation acquired from satellites. Medium and high spatial resolution multispectral data are often degraded by haze and haze detection and removal is still a challenging and important task. An empirical and automatic method for inhomogeneous haze removal is presented in this work. The dark object subtraction method is further developed to calculate a spatially varying haze thickness map. The subtraction of the haze thickness map from hazy images allows a spectrally consistent haze removal on calibrated and uncalibrated satellite multispectral data. The spectral consistency is evaluated using hazy and haze free remotely sensed medium resolution multispectral data.
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Aliaksei Makarau, Rudolf Richter, Rupert Müller, and Peter Reinartz "Spectrally consistent haze removal in multispectral data", Proc. SPIE 9244, Image and Signal Processing for Remote Sensing XX, 924422 (23 October 2014);

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