基于改进HLT与深度学习的双时相PolSAR洪涝灾害监测新方法  

A Novel Flood Disaster Monitoring Method based on Improved HLT and Deep Learning Using Bi-temporal PolSAR Images

作　　者：刘文宋 张仲英 郑琳 郭风成 LIU Wensong;ZHANG Zhongying;ZHENG Lin;GUO Fengcheng(School of Geography,Geomatics and Planning,Jiangsu Normal University,Xuzhou 221116,China)

机构地区：[1]江苏师范大学地理测绘与城乡规划学院,徐州221116

出　　处：《地球信息科学学报》2023年第8期1730-1745,共16页Journal of Geo-information Science

基　　金：国家自然科学基金项目(62201232、62101219);江苏省自然科学基金项目(BK20210921、BK20201026)。

摘　　要：全极化合成孔径雷达(PolSAR)通过主动收发不同极化方式的微波信号,可为全天时、全天候获取洪涝受灾信息提供有利的数据支持。然而,传统基于PolSAR影像的洪涝灾害监测方法受相干斑噪声影响严重,且洪涝灾害引发的变化类与非变化类的类别不平衡易导致灾害监测精度低。针对以上问题,本文提出了一种基于改进HLT与深度学习的双时相PolSAR洪涝灾害监测新方法。首先,通过构建一种顾及邻域信息的改进Hotelling-Lawley迹(HLT)统计量算子,减少PolSAR影像相干斑噪声及空间异质性对差异影像生成的影响;其次,为解决洪涝受灾区域变化类样本不足及变化类与非变化类不平衡的问题,引入双阶段中心约束FCM(TCCFCM)算法与深度卷积对抗生成网络(DCGAN)模型,形成一种稳健的洪涝灾害样本选择与扩充方法;最后,通过构建一种深度卷积小波神经网络(DCWNN)模型实现洪涝灾害精确监测。为了验证本文方法的可行性与鲁棒性,本文选取了2016年7月武汉梁子湖与严东湖洪涝灾害发生前后Radarsat-2影像进行实验。定性与定量评价结果表明:相对于传统的洪涝灾害监测方法,本文方法综合降低洪涝灾害监测的虚警率与漏警率分别为2%及1.5%左右,而监测洪涝灾害的总体精度与Kappa系数可分别提升3%及0.02左右,为相关部门应急救灾等工作提供技术支持。Rapidly and accurately monitor the flood disaster is very important,which can protect people's life and property safety and realize the sustainable development of society.The polarimetric synthetic aperture radar(PolSAR)image can obtain all-day and all-weather information of flood disaster by transmitting and receiving electromagnetic microwave of different polarizations,which can provide more favorable data support for flood disaster monitoring.However,the traditional flood disaster monitoring methods based on PolSAR images are seriously affected by speckle noise and the class imbalance between changed class and un-changed class leads to low accuracy of disaster monitoring.To address issues,a novel flood disaster monitoring method based on the improved Hotelling-Lawley Trace(HLT)statistic operator and deep learning for small area change using bitemporal PolSAR images was proposed in this paper.Within this method,the HLT statistic operator was firstly constructed by considering the neighborhood information of PolSAR images,which can reduce the influence of speckle noise and spatial heterogeneity for the generation of the difference image in this paper.Secondly,the Two-stage Center-Constrained Fuzzy C-Means clustering(TCCFCM)algorithm and the Deep Convolutional Generative Adversarial Network(DCGAN)were introduced to build a robust method of sample select and express when lacked the changed samples over disaster areas,which can solve the problem of the class imbalance between changed and un-changed class.Finally,the Deep Convolutional Wavelet Neural Network(DCWNN)was constructed to achieve accurate monitoring of flood disasters.In order to verify the feasibility and robustness of the proposed method,the Radarsat-2 images covered Wuhan city before and after flood disaster were selected in July,2016.The qualitative and quantitative results show that the proposed method can reduce the missed alarm rate and false alarm rate of flood disaster monitoring,and significantly improve the Overall Accuracy(OA)and Kappa coeffic

关 键 词：洪涝灾害 PolSAR影像 改进HLT算子 类别不平衡 双阶段中心约束FCM算法 深度卷积对抗生成网络 深度卷积小波神经网络 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TN957.52[自动化与计算机技术—控制科学与工程] TV87[电子电信—信号与信息处理]