Automated coronal hole segmentation from Solar EUV Images using the watershed transform

Region of interest segmentation in solar images is the subject of frequent research in solar physics. This study outlines watershed by immersion segmentation to identify coronal hole areas in solar images acquired using the Extreme UV Imaging Telescope (EIT). Solutions presented here produce highly accurate segmentation results of coronal holes of irregular shape, and what is more, they do so for images representing varied solar activity, recorded in different years and months. In addition, the solutions presented here make all the methods used operate very quickly. These methods include: the preprocessing step before the watershed segmentation, the watershed segmentation itself, and also the postprocessing of solar images after the watershed segmentation. The mean duration of the entire segmentation process of solar images amounts to 342 ms for a single coronal hole, without the parallel implementation of the methods used. The experiments were carried out on a computer with an Intel Core i7 CPU @ 2 GHz and 4 GB RAM. After the seed point is identified inside the coronal hole, the segmentation runs automatically.     

集成U-Net方法的无人机影像胡杨树冠提取和计数

塔里木河流域的胡杨林是该荒漠区域典型的森林资源,胡杨树冠大小和株数信息对塔里木河流域森林资源监测、生态保护和恢复具有重要意义。由于该流域乔灌草植物群落分布的复杂性,传统方法很难实现胡杨树冠的精准分割和大范围的株数提取。以塔里木河中游胡杨林为研究区,选取几块典型胡杨林区域,提出集成深度学习和分水岭分割的处理方法,对密集胡杨树冠的精准分割和单株胡杨的提取进行了深入探讨。首先,将无人机影像（空间分辨率0.16 m）无缝拼接生成正射影像;采用U-Net卷积神经网络对胡杨树冠覆盖区域进行精准分割;在U-Net模型分割的基础上使用标记分水岭方法对密集胡杨树冠进行自动再分割和单株计数,计算出所选研究区的胡杨株数并精准定位。结果表明U-Net卷积神经网络对胡杨的所有树冠区域提取的平均精度可达94.1%,在胡杨树冠覆盖区域识别分割的基础上,用标记分水岭分割方法对胡杨单木计算总体精度为93.3%。研究认为,结合深度学习和标记分水岭方法为自动化大范围森林资源监测提供了新思路和借鉴经验。     

Risk-based classification and interactive map of watersheds contributing anthropogenic stress to Laurentian Great Lakes coastal ecosystems

We describe development anthropogenic stress indices for coastal margins of the Laurentian Great Lakes basin. Indices were derived based on the response of species assemblages to watershed-scale stress from agriculture and urbanization. Metrics were calculated for five groups of wetland biota: diatoms, wetland vegetation, aquatic invertebrates, fishes, and birds. Previously published community change points of these assemblages were used to classify each watershed as ‘least-disturbed’, ‘at-risk’, or ‘degraded’ based on community response to these stressors. The end products of this work are an on-line map utility and downloadable data that characterize the degree of agricultural land use and development in all watersheds of the US and Canadian Great Lakes basin. Discrepancies between the observed biological condition and putative anthropogenic stress can be used to determine if a site is more degraded than predicted based on watershed characteristics, or if remediation efforts are having beneficial impacts on site condition. This study provides a landscape-scale evaluation of wetland condition that is a critical first step for multi-scale assessments to help prioritize conservation or restoration efforts.     

基于T-prim模型的肺气管分割算法

针对现有高阶支气管分割算法计算成本过大或分割精度不足等问题,提出一种基于T-prim模型的肺气管树分割算法。通过形态学灰度重建对CT图像进行初步处理,使用区域生长算法得到主支气管;从马尔可夫随机场的角度对分水岭算法分割框架进行优化,得到优化的分割框架;利用主气管骨架提取自动获得种子节点,算法迭代构造出T-prim模型,利用优化的分割框架得到完整的肺气管树。通过与两种EXACT09竞赛算法的对比实验证明了该算法在不依赖于种子点的人工选择,不需要训练集的条件下,能以极低的泄漏量获得更完整的分割结果。     

考虑蓄满产流机制的流域分布式产汇流模型

洪水预报通常基于降雨径流模拟进行。以山坡水文学理论的蓄满产流机制为出发点,展开了对山区流域降雨径流模拟的研究,以此建立分布式三水源产汇流模型。首先,对研究流域进行离散化,以数字高程数据为基础提取流域的地形数字特征,使离散化坡面单元的数据同时完成自动匹配,实现参数的空间离散化,为模型的应用提供参数支撑。其次,采用水流连续方程和达西公式计算单元坡面内的产流量;以圣维南方程组简化的运动波方程为控制方程,推导出坡面流近似的偏微分方程,利用特征线法对每一单元坡面进行汇流计算。所建分布式水文模型可模拟从非饱和壤中流漫溢到坡面单元上形成饱和坡面流的暴雨径流过程。同时借由马斯京根-康吉法衔接了坡面离散单元水文子过程与主河道的汇合。最后选取美国古德温河流域典型的降雨过程进行次洪过程模拟,采用确定性系数、相关系数来评定模拟精度,结果表明其模拟精度达到了径流过程模拟和作业预报的精度要求。总体来说,所建分布式产汇流模型不但适用于单一坡面的径流分析,且能够应用于一般流域以进行降雨径流过程的模拟,在一定程度上可为防洪调度提供参考依据。     

广州市东濠涌流域低影响开发措施雨洪控制效应评估

随着我国城市化进程的快速推进和海绵城市理念的不断加深,低影响开发(LID)措施日益受到各地重视。为评估LID措施的雨洪控制效果,构建基于SWMM的东濠涌流域城市雨洪模型,分析选取适合该研究区域的LID措施,分别采用重现期为0.5、1、2、5、10年的设计降雨对LID措施的雨洪控制效应进行分析。研究结果表明:LID措施能够较为有效地减小流域洪峰流量、径流量及推迟峰值时间,对初期雨水径流污染负荷也具有较好的削减作用,且径流量和污染物削减率均随着重现期增大而减小。     

红外目标的边界检测和标注

边界检测和标注是红外目标探测和识别的一个重要环节。自然背景复杂的纹理特征和红外图像中的噪声影响,以及红外目标模糊的边缘,给红外目标的边界检测和分割带来一定的困难。分水岭算法作为一种数学形态学的图像分割方法,为进行目标特征分析和识别提供了可能,然而,分水岭算法产生的过度分割,又为背景和噪声抑制增加了难度。文中在分水岭算法的基础上,结合模糊逻辑方法,对自然背景下的红外人工目标的边界检测和分割以及红外背景的抑制进行了探讨。     

海河流域洪水资源化利用初析

洪水资源化利用是发挥洪水资源特性,缓解水资源短缺,减少水旱灾害损失,实现水资源可持续开发利用的重要措施。针时海河流域自然地理、水系、暴雨洪水特性及现有防洪工程体系特点、水资源利用现状与问题,探讨了洪水资源安全利用的方向及潜力。     

Explanation for Anomalous Readings during Monitoring of a Best Management Practice

The USEPA’s Urban Watershed Management Branch (UWMB) has monitored storm-water drainage and best management practices (BMPs) as part of its overall research program. As part of this effort, continuous monitoring equipment was deployed to measure both storm events and periods between storms in a BMP. Of particular concern were electrodes used to measure ammonium (NH4+) concentrations. During the third deployment of the electrode there were readings of concern for NH4+ which exceeded expectations for drift or storm event response. The electrode indicated concentrations of NH4+ exceeded 2 mg/L, with a sustained maximum concentration of 8 mg/L recorded. These observations occurred between December 5, 2003 and December 10, 2003 after snow events. This period was marked by increased measures of conductivity measured which were contrary to typically low conductivity readings especially after precipitation events. Deicers (sodium chloride) spread on the roadway most likely caused increased NH4+ readings, as sodium ions are a known interference for NH4+. To back up this claim, controlled laboratory experiments were performed on the NH4+ electrode to test the response. Two tests were performed where salt was introduced into a bucket containing a probe. A determination was also made as to whether decaying leaves may have contributed to these elevated NH4+ readings. Results of the tests indicated that at low ammonia concentrations, salt is a measurable interference. Decaying leaves did increase NH4+ concentrations as measured by the electrodes, but only to 1 mg/L which coincided with both electrode readings and sample concentrations from the field.     

联合人体模型与块生长的人群目标分割

人群目标的准确分割,是进行多相机联合目标跟踪与识别的关键。该文首先构造包含位置、尺寸、姿态信息的人体粗略姿态模型,并利用贝叶斯模型获得对应的目标模型。然后将前景区域利用分水岭算法划分为颜色分布一致的子块,通过位置和颜色约束解决遮挡目标的种子块选取问题。最后通过块生长获得每个目标区域。对于颜色相似子块,通过比较其产生的边缘能量确定其所属目标。实验结果表明,本文能够实现对人群目标较精确分割,且对背景噪声具有一定的抗干扰能力。