Cloud Detection Based on Deep Learning Combining Muti-Feature for Remote Sensing Images

Abstract

The accurate detection of clouds in images is prerequisite for remote sensing image processing and applications. Traditional cloud detection methods rely on particular sensors, and the artificial neural network method only uses spectral or spatial information. In this paper, a novel method combining multiple features based on deep learning (MDL) for cloud detection is proposed. Deep neural network (DNN) and fully convolutional neural (FCN) network are applied to extract the spectral and spatial features of the images respectively, and the features are used for the input of another DNN for re-learning while the image data also serves as an input to the DNN. Finally, joint feature obtained by relearning is classified by Support Vector Machine (SVM). The method makes full use of the spectral-spatial information of the images to detect cloud comprehensively. A comparative experiment was carried on Landsat 8 images containing different types of clouds over various underlying surfaces. The results show that the MDL method performs favorably, which is significantly improved compared to the single neural network algorithm and the function of mask (FMask) algorithm.