Deep Convolutional Highway Unit Network For Land Cover Type Classification With GF-3 SAR Imagery

In recent years,with the rapid development of synthetic aperture radar(SAR)technology and satellites were launched,SAR data have ushered in the era of big data.As Chinese first C-band SAR sensor,GF-3 plays an important role in the earth science,climate change research,forest resources survey and other fields.SAR images interpretation under the era of big SAR data is a great challenge for scientific applications.At present,big data-based intelligent methods such as computer vision technology have achieved great success.Deep learning such as deep convolutional Highway Unit network has revolutionized the computer vision area.Deep learning-based algorithms have surpassed conventional algorithms in terms of performance by a significant margin.In this paper,deep learning will be applied to the work of GF-3 quad-pol SAR imagery classification.A deep convolutional Highway Unit network is employed to automatically extract a hierarchic feature representation from the data,based on which the land cover type classification can be conducted.The deep convolutional Highway Unit network by the SAR images,the proposed approach in the paper can reduce speckle,fully excavate the regularity of SAR images in time and space and effectively improve the accuracy of classification.Our experimental results,conducted on a commonly used GF-3 polarimetric SAR data,show that the proposed method provides classification results that are among the state-of-the-art.The research contents of this paper include the following three aspects:(1)The traditional convolutional neural network has become more difficult to train with the increase of network depth.In view of this,this paper improves the network structure.The construction of a deep convolution Highway Unit network by using the Highway learning Unit that can be repeatedly stacked,a way to solve the problems of the network information loss,which can train any deep network with fewer samples.(2)In order to evaluate the classification effect of the deep convolutional Highway Unit network,this paper uses the GF-3 quad-pol SAR image to conduct the experiment in the Yigen study area.Compared with traditional machine learning(Support vector machine and Random Forest),the average accuracy of the model classification results is 88.12%,and the classification accuracy of forest,grass and farm is over 90%.In addition,the pre-trained model is used to classify different time,different incidence angle and different sensors,so as to show that the generalization learning ability of the model under different conditions,providing new ideas and technical support for the fourth chapter of large-scale mapping.(3)A method based on deep convolution Highway Unit network to give full play to the ability of deep learning to deal with large data.This paper uses GF-3 dual-pol SAR data to realize large-scale mapping in Hulunbeier,and provides a good example for dealing with massive SAR data.