We discuss the appearance of systematic spatial and spectral patterns of noise in remotely sensed images as well as the possibility of mitigating the effects of these patterns on the data. We describe the structure of two simple theoretical models that predict the appearance of patterns of noise (mainly stripe noise). Moreover, two new algorithms that have been specifically developed to mitigate the noise patterns are described. The performance of the two algorithms is assessed by use of some hyperspectral images acquired by different kinds of airborne sensor. The algorithms show an unexpected ability to reject these noise patterns.
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