协同光学与雷达遥感数据的面向对象土地覆盖变化检测

Object-oriented land cover change detection combining optical and radar remote sensing data

西南地区受云、雨天气的影响,高质量的光学数据难以获取,合成孔径雷达数据可以全天时全天候地工作,但其重访周期较长,因此在多云多雨地区协同光学与雷达数据开展土地覆盖变化检测,可以弥补单一遥感数据源的不足,对国土资源调查评估及全球变化研究具有重要的意义。该研究基于2019年眉山地区Sentinel-2光学数据对2016年和2019年全极化Radarsat-2数据生成的差异影像进行面向对象引导分割,提取对象级多维特征,并提出FSO-RF变化检测框架,该框架利用样本间距离度量可分性特征空间优化(Feature Space Optimization,FSO)方法来优化特征空间,结合面向对象的随机森林(Random Forest,RF)分类器实现土地覆盖变化检测,与现有的变化检测算法对比,该研究的变化检测框架在精度上有较大提升,在试验区的准确率达到92.90%,通过抽取具有代表性的样区进行检验,2个样区变化检测结果的准确率分别为95.08%和88.16%。该研究提出的算法框架可以很好地满足城镇、农田等不同地物类别的变化检测需求,在国土资源监测中具有一定的应用价值。

High-quality optical data is difficult to acquire in southwest China,due to the cloudy and rainy weather.The synthetic aperture radar(SAR)can work for all-time and all-weather,but the revisit period is usually long.Therefore,it is a high demand for the synergy of optical and radar data rather than a single remote sensing data source,particularly for the survey and assessment on the land resource,as well as the global change.This study performed an object-oriented guided segmentation on the difference images generated from 2016 and 2019 quad-pol Radarsat-2 data using the 2019 Sentinel-2 optical data of Meishan region.A Fractal Net Evolution Approach(FNEA)was first utilized to segment the 2016 optical image for an initial segmentation.Then,the second segmentation was implemented using the same approach,where the features of the SAR image were provided for the purpose of guided segmentation.As such,the regions of growth were merged from small to large,in order to eventually form a complete geographical object.A final segmentation was obtained to select the changed and unchanged samples.More importantly,an object contained both changed and unchanged pixels,particularly with a single type of pixel inside the samples.Then,the object-level multidimensional features were extracted using the samples.The initial radar parameters included the backward scattering coefficient,Pauli and Freeman-Durden polarization decomposition parameters.The segmentation was utilized to generate the geographic objects.Meanwhile,some parameters were calculated using the initial parameters,including the mean,and standard deviation,whereas,the texture feature parameters using the Gray-level co-occurrence matrix(GLCM)included the mean,standard deviation,homogeneity,contrast,entropy,and the correlation for a total of 72-dimensional features.The redundant feature dataset was filtered to select the extracted feature set.Consequently,an FSO-RF change detection framework was proposed to achieve the feature optimization and the acquisition of the final change detection.The inter-sample distance metric learning was used to optimize the separability of feature space.The optimal features were used to calculate the distance between the changed and unchanged samples in different features.Finally,the change detection task was implemented using an object-oriented random forest classifier.The existing change detection pixel-based methods were selected to verify the improved model,including the change vector analysis(CVA),principal component analysis(PCA),multivariate alteration detection(MAD),Iteration multivariate alteration detection(IR-MAD),support vector machines(SVM),and random forest(RF)change detection using radar difference image segmentation.The change detection framework showed a remarkable improvement,with an overall accuracy of 92.90%in the test area.Two representative areas were selected in the study area for sampling and validation.The experiments demonstrated that better performance was achieved than that of the traditional,although the accuracy indexes were degraded in some areas with the complex features.The overall accuracies of the two sample areas reached 95.08%and 88.16%.The algorithm in this paper can well meet the needs of detecting changes in different feature categories such as towns and farmlands,and has certain application value in the monitoring of national land resources.