A synthetic aperture radar sea surface distribution estimation by n-order Bézier curve and its application in ship detection

To dates,most ship detection approaches for single-pol synthetic aperture radar(SAR) imagery try to ensure a constant false-alarm rate(CFAR).A high performance ship detector relies on two key components:an accurate estimation to a sea surface distribution and a fine designed CFAR algorithm.First,a novel nonparametric sea surface distribution estimation method is developed based on n-order Bézier curve.To estimate the sea surface distribution using n-order Bézier curve,an explicit analytical solution is derived based on a least square optimization,and the optimal selection also is presented to two essential parameters,the order n of Bézier curve and the number m of sample points.Next,to validate the ship detection performance of the estimated sea surface distribution,the estimated sea surface distribution by n-order Bézier curve is combined with a cell averaging CFAR(CA-CFAR).To eliminate the possible interfering ship targets in background window,an improved automatic censoring method is applied.Comprehensive experiments prove that in terms of sea surface estimation performance,the proposed method is as good as a traditional nonparametric Parzen window kernel method,and in most cases,outperforms two widely used parametric methods,K and G0 models.In terms of computation speed,a major advantage of the proposed estimation method is the time consuming only depended on the number m of sample points while independent of imagery size,which makes it can achieve a significant speed improvement to the Parzen window kernel method,and in some cases,it is even faster than two parametric methods.In terms of ship detection performance,the experiments show that the ship detector which constructed by the proposed sea surface distribution model and the given CA-CFAR algorithm has wide adaptability to different SAR sensors,resolutions and sea surface homogeneities and obtains a leading performance on the test dataset.     

广西桂林凉风洞秋冬季滴水和沉积物氧同位素对极端降水的记录研究

近年来,全球环境变化导致的极端气候事件的发生频率剧增,引起了国内外学者的广泛关注。由于器测记录时间较短,因此需要通过精确连续记录古气候变化的石笋来解译极端气候事件的规律。本文针对桂林凉风洞2015年9月至2016年1月的大气降水、洞穴滴水和现代沉积物的氧同位素组成进行了高分辨率的监测。监测结果揭示了由于秋冬水汽源的改变,导致桂林地区大气降水δD、δ~(18)O同位素值显示逐渐偏正的季节性变化,并且建立了该区域大气降水线为:δD=8.8δ~(18)O+16.38。由于大气降水作为凉风洞水源补给唯一来源,使得洞穴滴水的δ~(18)O继承大气降水的δ~(18)O所蕴含的环境信息,进而记录在洞穴现代沉积物中。但是受到洞穴顶部岩溶表层带的平滑或者均一化的作用,使得洞穴滴水的δ~(18)O同位素值要偏正于大气降水的δ~(18)O同位素值。在极端降水信号输入岩溶表层带后由于存在不同的水文地球化学过程,从而导致洞穴沉积物对其极端降水事件的响应时间存在不同,并且在δ~(18)O同位素值也存在明显差异。因此,系统监测洞穴滴水、沉积物δ~(18)O同位素对外界环境信息的响应过程,能为量化或精确解译石笋δ~(18)O同位素记录所指代的古气候环境信息提供基础。     

月表构造与深部构造的关系

地球和月球很可能是通过大撞击形成的。在行星地质学中,研究月球的地质-构造现象,对了解月球、地球乃至太阳系的形成与演化历史都有很大帮助。月球的构造分为深部构造与月表构造,寻找它们在分布或成因上的关系,可以为月球甚至地月系的起源和演化历史提供重要参考。利用LROC的宽视角影像数据以及LOLA数据提取解译月表构造,结合深大断裂进行观察分析,并对月球的撞击盆地进行统计,最后以静海地区为例分析构造分布特征,发现月球的质量瘤盆地中具有环状分布的月岭,外侧具有近环状分布的深大断裂,自前酒海纪至酒海纪,具备上述特征的质量瘤盆地占总撞击盆地的比例突然有一个很大的提升,且静海地区西部具有该构造分布特征。推测该特征与撞击、月海沉降等有关,且在酒海纪与雨海纪期间月球有较多的月海玄武岩分布,由此判断静海西部存在质量瘤,发生过撞击与月海沉降。     

三江源区大气可降水量时空特征及其与降水关系

利用1979—2016年欧洲中期天气预报中心(ECMWF) ERA-Interim (1°×1°)再分析资料中的经、纬向水汽通量和大气可降水量(precipitation water vapor,PWV)数据,采用相关性分析、趋势分析法、累积距平、IDW等方法,分析三江源地区PWV与水汽通量的时空分布特征、降水转化率(precipitati-on conversion efficiency,PCE)变化规律。结果表明:过去的38 a,经、纬向多年平均水汽通量分别为50. 2、196. 7 kg·m-1·s^(-1),纬向水汽通量气候倾向率比经向大。南边界为纬向主要水汽输入边界,东边界为经向主要水汽输出边界,纬向水汽输送大于经向输送。多年平均PWV为1998. 3 mm,近38 aPWV呈现微弱增加趋势,1979—1997年,PWV呈下降趋势,1998年后PWV呈增加趋势,同期降水也在增加,说明该时段三江源地区气候转湿。PWV与水汽通量的年际变化趋势和转折年相一致。三江源区多年平均PCE为24. 57%,1989年PCE最高,达32. 76%,各季节平均PCE空间分布与年平均PCE分布一致,均表现出南部、东南部高,西部、东北部低的变化特征,各季节PCE大小差异明显,春季多年平均PCE为15. 92%,夏季25. 67%,秋季21. 01%,冬季仅7. 03%。     

地理信息技术发展的新方向——网格GIS初探

网格GIS是网格及网格计算技术与GIS相结合而形成的新的GIS发展方向,具有空间资源共享、海量空间数据存储与处理、异地协同工作与异构系统支持等功能,在城市管理、房地产管理、资源勘测与调查等方面具有十分重要的作用。本文在分析网格GIS的概念及其特征的基础上,提出了网格GIS的四层结构体系,并简要介绍了网格GIS实现的关键技术。     

中国大陆地壳运动与强震关系研究

利用GPS观测结果研究了中国大陆当前地壳运动的空间分布及其所揭示的大区域构造变形背景与趋势;讨论了区域水平运动、应变率场分布与强震地点的关系;通过对昆仑山口西Ms8.1等地震区域水平运动与形变的分析,研究了强震过程的区域形变场变化的主要特征;提出了识别孕震形变场以进行中长期强震地点预测的思路和途径。     

区域性森林大火的真正成因

森林大火真正起因于地球排气中易燃还原性气体在腐殖层的临界积聚(气体体积分数大约为5%~6%)。现场气体(H2,CO)系统测量结果证实了这一点。测量结果显示,该林区腐殖层中H2、CO质量分数分别为(10~170)×10-6(本底值为0·5×10-6)和(2~60)×10-6(本底值为0·5×10-6),二者均远高于大气本底值数十到数百倍,而且未燃区的测值显然高于1998年燃区。     

盐津5.1级地震现场调查与烈度分布

对盐津5．1级地震现场38个居民点、20所学校、8个卫生院（室）及计生站、21项生命线工程及水利工程结构、文化旅游设施和铁路等的震害调查资料进行了分析,得出了本次地震的等烈度分布及有关参数。分析了场地条件、断层、震害异常原因及各类房屋、生命线工程、水利设施等的震害特征和地质灾害特点,并提出了灾区恢复重建的减灾措施和建议。     

Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer

As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency.     

基于多源数据融合的山地实况业务体系建设

【目的】探讨和实践基于多源数据融合的山地实况业务体系建设,以提高山地地区的监测、预警和灾害应对能力。【方法】通过分析山地地区面临的地理环境、气象条件以及自然灾害的特点,以及基于对已有实况业务发展的现状分析,建设全面、精准、实时的山地实况业务体系。【结果】明确贵州省实况产品建设方向并介绍了在山地实况业务体系建设中的技术路线,包括数据采集、传输、处理、展示、全流程监控等关键步骤,以及各环节的技术创新和方法改进。最后分析了面向重大服务、重点行业、重要区域开展有针对性的实况产品服务时仍需重点解决的问题。【结论】该文的研究成果为山地区域的实况业务体系建设提供了思路和方法,对于提升山地区域的灾害管理水平具有实际应用价值。