SAR image despeckling with a multilayer perceptron neural network

作     者：Xiao Tang Lei Zhang Xiaoli Ding 

作者机构：Department of Land Surveying and Geo-informaticsThe Hong Kong Polytechnic UniversityKowloonHong Kong 

出 版 物：《International Journal of Digital Earth》 (国际数字地球学报（英文）)

年 卷 期：2019年第12卷第3期

页      面：354-374页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

主　　题：Multilayer perceptron synthetic aperture radar despeckling neural network 

摘      要：Speckle noise in synthetic-aperture radar (SAR) images severely hindersremote sensing applications;therefore, the appropriate removal ofspeckle noise is crucial. This paper elaborates on the multilayerperceptron (MLP) neural-network model for SAR image despeckling byusing a time series of SAR images. Unlike other filtering methods thatuse only a single radar intensity image to derive their parameters andfilter that single image, this method can be trained using archivedimages over an area of interest to self-learn the intensitycharacteristics of image patches and then adaptively determine theweights and thresholds by using a neural network for imagedespeckling. Several hidden layers are designed for feedforwardnetwork training, and back-propagation stochastic gradient descent isadopted to reduce the error between the target output and neuralnetwork output. The parameters in the network are automaticallyupdated in the training process. The greatest advantage of MLP is thatonce the despeckling parameters are determined, they can be used toprocess not only new images in the same area but also images incompletely different locations. Tests with images from TerraSAR-X inselected areas indicated that MLP shows satisfactory performance withrespect to noise reduction and edge preservation. The overall imagequality obtained using MLP was markedly higher than that obtainedusing numerous other filters. In comparison with other recentlydeveloped filters, this method yields a slightly higher image quality,and it demonstrates the powerful capabilities of computer learningusing SAR images, which indicate the promising prospect of applyingMLP to SAR image despeckling.