论文标题
通过小波转换机器学习和深度学习的云检测
Cloud Detection through Wavelet Transforms in Machine Learning and Deep Learning
论文作者
论文摘要
云检测是使用远程感知的数据对图像识别和对象检测的专业应用。该任务提出了许多挑战，包括分析在可见，红外和多光谱频率中获得的图像，通常没有地面真相数据进行比较。此外，应用于此任务的机器学习和深度学习（MLDL）算法必须在计算上有效，因为它们通常部署在低功率设备中，并被要求实时操作。 本文解释了小波变换（WT）理论，将其与更广泛使用的图像和信号处理变换进行了比较，并探讨了WT用作MLDL分类器的强大信号压缩机和特征提取器的使用。
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.
