An improved building detection approach using L-band POLSAR two-dimensional time-frequency decomposition over oriented built-up areas

The objective of this study is to efficiently extract detailed information about various man-made targets in oriented built-up areas using polarimetric synthetic aperture radar (POLSAR) images. This paper develops an improved approach for building detection by utilizing Two-Dimensional Time-Frequency (2-D TF) decomposition. This method performs outstandingly in distinguishing between man-made and natural targets based on the isotropic behaviors, frequency-sensitive responses, and scattering mechanisms of objects. The proposed method can preserve the spatial resolution and exploit the advantages of TF decomposition; specifically, the exact outlines of buildings can be effectively located, and more types of features (e.g., flat roofs, roads, and walls that are oblique to the radar illumination) can be distinguished from forests in complex built-up areas by 2-D TF decomposition. The coarser-resolution subaperture images that are produced in the azimuth direction, which correspond to different looking angles, are beneficial for detecting man-made structures with main scattering centers oriented at oblique angles with respect to the radar illumination. In the range direction, the obtained subaperture images, which correspond to various observation frequencies, can be helpful in distinguishing flat roofs and roads from forests. This method was successfully implemented to analyze both NASA/JPL L-band AIRSAR and L-band EMISAR data sets. The building detection results of the proposed method exhibit a significant improvement over those of other methods and reach an overall accuracy over 80%, with approximately 20% higher than the accuracies of K-means clustering and the entropy/alpha-Wishart classifier and approximately 10% higher than the accuracy of the support vector machine method. Moreover, building details can be precisely detected, obliquely oriented buildings can be identified, and the distinction between buildings and forests is significantly improved, as both visually and statistically indicated. This method is highly adaptable and has substantial application value.     

基于时间序列叶面积指数稀疏表示的作物种植区域提取

以华北平原黄河以北地区为研究区域,以时间序列叶面积指数LAI(Leaf Area Index)傅里叶变换的谐波特征作为不同作物识别的数据源,利用稀疏表示的分类方法识别2007年—2016年冬小麦、春玉米、夏玉米等主要农作物种植区域。首先利用上包络线Savitzky-Golay滤波分别对2007年—2016年的时间序列MODIS LAI曲线进行重构,进而对重构的年时间序列LAI进行傅里叶变换,以0—5级谐波振幅、1—5级谐波相位作为作物识别的依据,基于各类地物的训练样本,通过在线字典学习算法构建稀疏表示方法的判别字典,对每个待测样本利用正交匹配追踪算法求解稀疏系数,从而计算对应于各类地物的重构误差,根据最小重构误差判定待测样本的作物类型,并对作物识别结果的位置精度进行验证。结果表明,2007年—2016年作物识别的总体精度为77.97%,Kappa系数为0.74,表明本文提出的方法可以用于研究区域主要作物种植区域的提取。     

Understanding geographical patterns of a city’s diurnal rhythm from aggregate data of location‐aware services

Do collective behaviors of the daily routine of a city's inhabitants form the periodical cycling of human activity at the city level (here termed the “city's diurnal rhythm”)? If the answer is yes, do there exist geographical patterns in the city's diurnal rhythm? Using a nationwide dataset of observed uses of location‐aware services in the largest Chinese social media platform, we first confirm the significant periodicity in city‐level human activity from the perspective of the aggregate degree of social media uses over a day. We then investigate geographical changes in the diurnal rhythm of human activity and its local variations in different parts of the city, and between weekdays and weekend days, over 340 Chinese cities. Our results show that a city's diurnal rhythm across the whole country exhibits both regular, nationally conspicuous shifts along geographical gradients and locally distinct spatiotemporal changes within the city. Our findings could provide insights into the characterization of the daily routine of city‐level human activity and its geographical patterns, and have potential for several issues in terms of planning, management, and decision‐making related to human population dynamics.     

SAR image despeckling with a multilayer perceptron neural network

Speckle noise in synthetic-aperture radar (SAR) images severely hinders remote sensing applications; therefore, the appropriate removal of speckle noise is crucial. This paper elaborates on the multilayer perceptron (MLP) neural-network model for SAR image despeckling by using a time series of SAR images. Unlike other filtering methods that use only a single radar intensity image to derive their parameters and filter that single image, this method can be trained using archived images over an area of interest to self-learn the intensity characteristics of image patches and then adaptively determine the weights and thresholds by using a neural network for image despeckling. Several hidden layers are designed for feedforward network training, and back-propagation stochastic gradient descent is adopted to reduce the error between the target output and neural-network output. The parameters in the network are automatically updated in the training process. The greatest advantage of MLP is that once the despeckling parameters are determined, they can be used to process not only new images in the same area but also images in completely different locations. Tests with images from TerraSAR-X in selected areas indicated that MLP shows satisfactory performance with respect to noise reduction and edge preservation. The overall image quality obtained using MLP was markedly higher than that obtained using numerous other filters. In comparison with other recently developed filters, this method yields a slightly higher image quality, and it demonstrates the powerful capabilities of computer learning using SAR images, which indicate the promising prospect of applying MLP to SAR image despeckling.     

珠一坳陷始新世文昌组沉积期构造转换对源‒汇体系的控制

珠江口盆地珠一坳陷在中始新世裂陷期文昌组沉积早、晚期之间存在明显的构造转换,该构造事件对古近系源?汇体系的形成和演变有重大影响.基于研究区地震、钻井、测井等资料的综合分析,本文认为构造转换控制了裂陷期源?汇体系的盆山地貌、物源供给、搬运方向及沉积样式等基本要素,并影响深层古近系储层条件.而且在构造转换的制约下,研究区发...     

论海相页岩气富集机理——以四川盆地五峰组—龙马溪组为例

深入探究页岩气富集机理是保障勘探开发高效推进的基础。本研究通过对四川盆地五峰组—龙马溪组页岩气勘探开发实践的系统分析,梳理总结前人研究成果,从生成机理、运移机理、赋存机理和保存机理四个方面对海相页岩气富集机理进行了深入分析,并讨论了深层和常压页岩气的勘探开发潜力。结果表明：在生成机理方面,埋藏史和热演化史控制了页岩生排烃史、生排烃量和现今含气量;页岩气运移机理涉及运移动力、运移相态、运移方式和运移通道四方面内容,页岩气运移主要是烃源岩内的初次运移,同时讨论了初次运移的影响因素;在赋存机理方面,甲烷—页岩间表现出单/多分子层吸附和微孔充填等多种赋存机制,组分润湿性和孔隙有效性是决定甲烷吸附赋存和解吸运移的关键;在保存机理方面,盖层和物性自封闭是主要的保存机理,构造运动引起的裂缝—流体活动是页岩气保存条件遭到破坏的主要原因,流体活动时间和期次研究是页岩气保存条件和含气量定量评价的重要内容。页岩气富集机理的系统分析和创新认识为页岩气勘探开发评价提供了重要依据,建议加强页岩气演化历史全过程的动态评价。结合深层和常压页岩气勘探实践,分析了深层和常压页岩气的成因机制及主要特征,指出了下一步攻关内容及勘探方向。     

科尔沁沙地粗—细组分的碎屑锆石U- Pb年龄特征：对定量物源及区域构造- 岩浆演化事件的指示

追踪干旱—半干旱地区大型沙地的物源和风- 河流相互作用机制对于理解陆地景观格局演变、地表过程与地貌动态以及大气圈和岩石圈之间的联系至关重要。然而,目前科尔沁沙地的物源仍存在较大争议,且缺乏具有统计意义的锆石U- Pb年龄数据库。因此,本研究对科尔沁沙地地表风成沙进行多点取样,根据不同粒级(＜63 μm和＞63 μm)选取1500颗碎屑锆石进行U- Pb测年分析,并利用逆向蒙特卡罗模型对其物源进行定量约束。结果表明,从目视定性的角度看,科尔沁沙地的碎屑锆石U- Pb年龄谱特征非常相似。但定量重建结果显示沙地的物源整体上以中亚造山带的贡献为主(50. 5%~61. 3%),然而东南部体现出华北克拉通的绝对优势(~75. 8%)。科尔沁沙地的物源存在空间异质性,沙地西部和北部的锆石年龄谱极为相似,与南部锆石年龄特征显著不同。科尔沁沙地的碎屑锆石U- Pb年龄特征基本不受粒度分异的影响,但沙地东南部除外。本文认为,风与河流的协同作用及其由此导致的沉积分异和再循环作用解释了科尔沁沙地碎屑锆石的U- Pb年龄特征。结合区域构造演化历史,科尔沁沙地在~2. 5 Ga和~1. 85 Ga的U- Pb年龄峰值分别是前寒武纪华北克拉通的生长、拼合与碰撞过程中的两期构造事件的产物。此外,~1. 7 Ga的锆石年龄可能是对Columbia超大陆聚合与裂解的综合响应。古生代以来的锆石年龄峰值(500~400 Ma、300~250 Ma、130~110 Ma)记录了在古亚洲洋的俯冲闭合、蒙古- 鄂霍次克海俯冲碰撞和古太平洋的俯冲、回退的区域构造背景下多期区域构造- 岩浆事件。     

天津市海洋经济中港口产业发展研究

作为海洋经济的重要组成部分,港口经济的发展为天津市海洋经济的跨越式发展提供了非常有利的契机。要尽快实现天津港口经济健康、有序、可持续的发展,为天津滨海新区的开发开放提供更强有力的支持,从而实现建设北方国际航运中心和国际物流中心的目标。综合近年来海洋统计的成果,以AHP法为分析方法,对天津港口经济的现状和发展趋势作探讨和研究,指出天津港口经济在未来发展中需要注意的问题,并提出初步解决方案。     

渤海湾表层沉积物重金属与污染评价

用原子吸收法测定了渤海湾表层沉积物中重金属的含量,同时用2种评价方法对该海域底质环境进行了评价。结果表明:沉积物中汞、锌含量平均值超过标准,其余项目未发现超标;其分布特征由于受沿岸不同污染源的污染影响,重金属污染物难以形成一定规律的分布状态。同时该海域底质环境质量总指数已达到污染程度和很强的生态危害,其中主要污染元素是Hg,已达重污染,并形成极强的生态危害。     

基于FY-4A卫星和随机森林算法的西藏山南市雷电短临预报方法

针对青藏高原地区雷电短临预报缺乏雷达资料的问题,采用FY-4A卫星多通道数据、欧洲中心第5代再分析资料(ERA5)中的对流指数、闪电定位仪资料等多源监测数据,根据雷电的发生、发展机理,提出了18个关键预报因子,利用随机森林算法建立了适用于西藏山南地区的雷电短临预报模型。统计分析各预报因子在有无雷电天气样本中的概率密度分布与随机森林模型得到的特征重要度指标,结果表明提出的预报因子物理意义明确,建立的模型可信度较高。利用随机森林算法分别对未来10 min、20 min、30 min建立雷电预报模型,并与光流外推预报方法进行对比检验,结果表明：随机森林模型预报效果命中率(POD)、临界成功指数(CSI)均高于光流法,空报率(FAR)也相对较低;未来20 min的随机森林预报模型CSI评分最高,整体预报效果最佳。