基于深度置信网络-Otsu混合模型的自动云检测算法

Automatic cloud detection algorithm based on deep belief network-Otsu hybrid model

地球表面一半以上被云覆盖,卫星遥感图像中的云检测主要是人工检测识别或者半自动化方法,依赖人工干预,效率不高,难以满足实时或准实时应用的需要。为了提高卫星遥感数据的可用性,基于深度置信网络(DBN)和最大类间方差法,提出一种自动云检测算法——DOHM。该算法采用自适应阈值代替人工标定阈值,实现云检测的全自动化,将云检测的正确率提高到95%以上;DOHM算法选取了维度为9的特征向量作为检测网络的输入,输入特征向量的多样性,有利于网络更全面有效地捕捉到云的特点。

More than half of the earth’s surface is covered by cloud.Current cloud detection methods from satellite remote sensing imageries are mainly manual or semi-automatic,depending upon manual intervention with low efficiency.Such methods can hardly be utilized in real-time or quasi real-time applications.To improve the availability of satellite remote sensing data,an automatic cloud detection method based on Deep Belief Network(DBN)and Otsu’s method was proposed,named DOHM(DBN-Otsu Hybrid Model).The main contribution of DOHM is to replace the empirical fixed thresholds with adaptive ones,therefore achieve full-automatic cloud detection and increase the accuracy to greater than 95%.In addition,a 9-dimensional feature vector is adopted in network training.Diversity of the input feature vector helps to capture the characteristics of cloud more effectively.