Cloud Detection through Wavelet Transforms in Machine Learning and Deep Learning
This work addresses cloud detection for remote sensing applications, but it appears incremental as it focuses on applying an existing transform to a known problem.
The paper tackles cloud detection in remotely sensed images by exploring Wavelet Transform as a feature extractor for machine learning and deep learning classifiers, aiming to improve computational efficiency for real-time deployment on low-power devices.
Cloud detection is a specialized application of image recognition and object detection using remotely sensed data. The task presents a number of challenges, including analyzing images obtained in visible, infrared and multi-spectral frequencies, usually without ground truth data for comparison. Moreover, machine learning and deep learning (MLDL) algorithms applied to this task are required to be computationally efficient, as they are typically deployed in low-power devices and called to operate in real-time. This paper explains Wavelet Transform (WT) theory, comparing it to more widely used image and signal processing transforms, and explores the use of WT as a powerful signal compressor and feature extractor for MLDL classifiers.