基于Mult-TWDTW算法的时序SAR图像土地覆盖分类

Land Cover Classification of Time-series SAR Images Using Mult-TWDTW Algorithm

合成孔径雷达(SAR)图像为土地覆盖分类提供了重要的时序数据源.现有的时间序列匹配算法可以充分挖掘时序特征的相似性信息,从而获得较好的分类效果.本文引入了综合考虑形状相似性和物候差异的经典时序匹配算法TWDTW (time weighted dynamic time warping)指导SAR土地覆盖分类,并针对传统TWDTW仅考虑单一特征时间序列上的相似性匹配问题,提出了一种基于多特征联合的时间加权动态时间规整算法(Mult-TWDTW).该方法首先提取后向散射系数、干涉相干性以及双极化雷达植被指数(dual polarization radar vegetation Index,DpRVI)这3种特征,然后在TWDTW算法基础上联合多个特征设计了Mult-TWDTW模型.为验证所提方法的有效性,使用Sentinel-1A时序数据在丹江口区域完成土地覆盖分类,并将Mult-TWDTW与MLP、1D-CNN、Kmeans、SVM和使用单特征的TWDTW算法进行对比.实验结果显示,Mult-TWDTW算法得到了最好的分类效果,总体精度和Kappa系数可以达到95.09%和91.76,表明Mult-TWDTW算法有效联合了多个特征信息,能够提升时序匹配算法在多种土地覆盖类别分类中的潜力.

Synthetic aperture radar(SAR) images provide an important time-series data source for land cover classification.The existing time-series matching algorithms can fully exploit the similarity among time-series features to obtain satisfactory classification results.In this study,the classic time-series matching algorithm named time-weighted dynamic time warping(TWDTW),which comprehensively considers shape similarity and phenological differences,is introduced to guide SAR-based land cover classification.To solve the problem that the traditional TWDTW algorithm only considers the similarity matching of a single feature on the time series,this study proposes a multi-feature fusionbased TWDTW(Mult-TWDTW) algorithm.In the proposed method,three features,namely,the backscattering coefficient,interferometric coherence,and the dual-polarization radar vegetation index(DpRVI),are extracted,and the Mult-TWDTW model is designed by fusing multiple features based on the TWDTW algorithm.To verify the effectiveness of the proposed method,the study implements land cover classification in the Danjiangkou area using timeseries data obtained from the Sentinel-1A satellite.Then,the Mult-TWDTW algorithm is compared with the multi-layer perception(MLP),one-dimensional convolutional neural network(1D-CNN),K-means,and support vector machine(SVM) algorithms as well as the TWDTW algorithm using a single feature.The experimental results show that the MultTWDTW algorithm obtains the best classification results,manifested as its overall accuracy and Kappa coefficient reaching 95.09% and 91.76,respectively.In summary,the Mult-TWDTW algorithm effectively fuses the information of multiple features and can enhance the potential of time-series matching algorithms in the classification of multiple types of land covers.