Investigation of the ground displacement in Saint Petersburg,Russia, using multiple-track differential synthetic aperture radar interferometry

Global sea level rise and local land subsidence might exacerbate the risk of flooding in coastal plains. Among other cities, this is also the case for the high-latitude city of St. Petersburg, which has long been threatened by flood events. To protect the urban area from storm surges, the Union of Soviet Socialist Republics (USSR) in 1978 approved the construction of the 25 km long Flood Prevention Facility Complex (FPFC), which was completed in 2011. The risk of flooding in the city area of St. Petersburg is amplified by the fact that large sections of the coastal area have been reclaimed from the sea. In this study, we investigate the temporal evolution of the ground displacement in St. Petersburg. To this end, we perform an extended analysis based on the application of a simplified version of the differential interferometric synthetic aperture radar technique, known as the minimum acceleration (MinA) approach. The MinA algorithm is a multi-satellite/multi-track interferometric combination technique that allows working with multiple sets of SAR images. The method allowed generation of time series of two-dimensional (2-D) (i.e. East-West and Up-Down) deformation of the terrain by processing two sequences of Sentinel-1A/B (S-1A/B) SAR images acquired from 2016 to 2018, along the ascending and descending flight passes. The Small BAseline Subset (SBAS) algorithm was independently applied to the two sets of SAR data to generate the relevant Line-Of-Sight (LOS)-projected ground deformation time series. Subsequently, the LOS-projected deformation products were geocoded and jointly combined. The results indicate that the deformation in the city is predominantly vertical (i.e. it is subsiding) with a maximum subsidence rate of about 20 mm/year corresponding to the newly sea-reclaimed lands. Finally, the error budget of the retrieved 2-D deformation time series has also been addressed.     

Hierarchical classification with subsequent aggregation of heathland habitats using an intra-annual RapidEye time-series

Mapping heathland habitats is generally challenging due to fine-scale habitats as well as spectral ambiguities between different classes. A multi-seasonal time-series of multispectral RapidEye data from several phenological stages was analysed towards the classification of different vegetation communities.A 3-level hierarchical dependent classification using Import Vector Machines was tested, based on the assumption that a probabilistic output per class would help the mapping. The first level of the hierarchical classification was related to the moisture gradient, which was derived from Ellenberg’s moisture indicative value. The second level aimed to separate plant alliances; the third level differentiated individual plant associations.For the final integration of the three classification levels, two approaches were implemented: (i) the F1-score and (ii) the maximum classification probability. The overall classification accuracies of both methods were found to be similar, around 0.7.Nevertheless, based on our expert knowledge we found the probabilistic approach to provide a more realistic picture and to be more practical compared to the result using the F1-score from the management point of view. In addition, the overall performance of the maximum probabilistic approach is better in the sense that the same accuracy of 0.7 was achieved with a differentiation of 33 classes instead of only 13 classes for the F1-score, meaning that the method is able to separate more spectral classes at a more detailed level providing the same accuracy.     

栈式稀疏自编码网络的多时相全极化SAR散射特征降维

利用极化合成孔径雷达（PolSAR）能够实现地物的识别和分类,而多时相全极化SAR可以获取地物更多的散射特征,提升地物识别精度,但高维散射特征的引入会带来严重的维数灾难问题。为了实现对高维散射特征的有效降维,本文提出一种基于栈式稀疏自编码网络S-SAE（Stacked Sparse AutoEncoder）的多时相PolSAR散射特征降维方法。该方法首先对PolSAR数据进行极化目标分解以获取高维散射特征;然后使用S-SAE对获取的多维特征进行降维处理,其中S-SAE降维方法首先采用无监督训练方式进行逐层贪婪训练;再结合Sigmod分类器,利用监督训练的方式对S-SAE进行参数优化,实现高维特征的有效降维;最后以降维后的特征作为支持向量机（SVM）和卷积神经网络（CNN）分类器的输入,实现地物分类。通过仿真和实测的两组多时相Sentinel-1数据处理结果表明,双隐层的S-SAE降维方法在各分类器上均取得最优的降维效果;对比各降维方法在SVM分类器上的分类精度,S-SAE较于局部线性嵌入（LLE）与主成分分析（PCA）降维方法,总体分类精度分别至少提升了9%和14%;在CNN分类器上,S-SAE较于LLE与PCA降维方法,总体分类精度分别至少提升了7%和9%。     

义能矿区开采沉陷演化的TS-DInSAR监测与影响分析

为研究济宁市义能矿区开采沉陷演化特点及其对周边建筑物的影响,本文采用69景长时间序列中等分辨率Sentinel-1影像,以TS-DInSAR技术体系中的小基线集DInSAR分析为研究方法,获取了2016年初至2018年底该矿区开采面周边的沉降信息。结果表明,义能矿区在监测时段内沉陷范围较为集中,最大累计沉降量约为627 mm,通过居民点位置与沉陷结果的叠加分析,发现周边3处村庄受到了不同程度的影响,该结果能为煤矿开采沉陷控制方案的效果评估及设计优化提供重要参考作用。     

基于特征提取的光学影像和SAR影像自动配准

针对现有的配准方法用于多光谱影像与SAR遥感影像配准时,存在受SAR图像斑纹噪声影像大、手工选取配准控制点精度低、利用图像景物特征配准时获取区域和边沿困难等问题,以SPOT5影像与RADARSATSAR影像配准进行实验,提出了一种利用改进的SIFT在提取的特征图像上寻找匹配点进行粗配准,然后利用交叉累积剩余熵作为相似性测度结合原始影像信息寻找光学特征图像的角点在SAR影像上的匹配点并进行精配准的方法,配准精度达到了子像素级水平。实验结果表明该方法对多源遥感影像有很强的适应性,配准精度高。     

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Submesoscale eddies in the East China Sea detected from SAR images

Seven-year(2005–2011) Synthetic Aperture Radar(SAR) images are applied to study oceanic eddies in the East China Sea. It is found that most of these eddies detected from the SAR images are less than 10 km, which are submesoscale eddies. Seasonal differences are evident in the distribution of eddies, with the highest and the lowest number of eddies noted in summer and winter, respectively. Since slick streaks in SAR images look dark, an eddy identified due to the slicks is referred to as "black eddy". As a result of wave-current interactions in the zones of current shear, it can be seen that an eddy exhibits a bright curve, the eddy is called "white eddy". During the seven years, 95 black eddies and 50 white eddies are identified in the study area. Black eddies are found in the whole study area while white eddies are mainly distributed in the vicinity of the Kuroshio Current. This study suggests that the distribution of the white eddy is denser around the Kuroshio because of the strong shear in the Kuroshio region. In terms of the eddy sizes, white eddies are generally smaller than black eddies.     

隆昌州—上麻屯一带1:5万地质调查中的物探解释

1:5万地质调查中的综合物探工作包括综合剖面测量、岩石(地层)磁性测试,以及重、磁场资料的处理等。应用重、磁场特征,划分重、磁场的地质分区,确定地质体,地质构造的划分标志,印证地质填图中的结果和推断深部地质体和地质构造,完成深部地质图。1:5万地质填图中综合物探工作的开展,提高了地质图的精度,为深部地质研究提供了依据。     

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NaCI—CaCl2-H2O体系人工合成流体包裹体及其冷冻后的熔融行为研究

在倪培等在2003年和丁俊英等在2005年成功开展人工合成包裹体研究的基础上，利用愈合人工水晶裂隙技术，在设定的实验条件下合成了NaCl-CaCl2-H2O体系的流体包裹体。对合成的包裹体进行了岩相学观察，显微测温工作，并结合相应相图分析研究——主要对合成包裹体的低温熔融行为进行探讨。结果表明合成的包裹体捕获了既定组成的流体，其低温相变过程与相应相图吻合。因此，人工合成流体包裹体与天然包裹体相似，天然流体包裹体的各种行为可以参照人工合成流体包裹体；同时，通过人工合成流体包裹体校正补充实验相图，使其更准确的被应用于天然包裹体测定分析。