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Cloud Detection through Wavelet Transforms in Machine Learning and Deep Learning

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Document pages: 4 pages

Abstract: Cloud detection is a specialized application of image recognition and objectdetection using remotely sensed data. The task presents a number of challenges,including analyzing images obtained in visible, infrared and multi-spectralfrequencies, usually without ground truth data for comparison. Moreover,machine learning and deep learning (MLDL) algorithms applied to this task arerequired to be computationally efficient, as they are typically deployed inlow-power devices and called to operate in real-time.This paper explains Wavelet Transform (WT) theory, comparing it to morewidely used image and signal processing transforms, and explores the use of WTas a powerful signal compressor and feature extractor for MLDL classifiers.

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