番红砗磲外套膜色素提取条件优化及成分分析

以番红砗磲(Tridacna crocea)为实验对象,通过单因素对比实验对其外套膜色素的提取条件进行了优化,并利用薄层层析色谱法对色素组成进行了初步分析。结果表明:丙酮和乙醇为适宜的番红砗磲外套膜色素提取剂;使用丙酮为提取剂时,浸提时间为4 h,提取温度为25℃,料液比为1︰40。使用乙醇为提取剂时,浸提时间为4 h,提取温度为30℃,料液比为1︰200;利用薄层色谱法分离外套膜颜色为蓝色和红棕色的番红砗磲外套膜色素后发现,蓝色番红砗磲外套膜色素由9种色素组分构成,红棕色个体番红砗磲外套膜色素由10种色素组分构成。     

大面积水深异常检测的条件变化自编码算法

针对大面积海底地形数据缺失或异常的复杂及多变性特点,结合条件变分自编码器(CVAE)与深度卷积生成对抗网络(DCGAN),构建了条件变分自编码生成对抗网络(CVAE-GAN)大面积海底伪地形的检测与剔除方法。本文方法利用条件变分自编码算法改变原有的样本分布,通过对训练样本的学习重新构建样本之间的分布规律,有效提高了高维到低维映射的稳定性;结合生成对抗网络,提高了整体算法的稳健性,最终得到较优的检测与剔除结果。采用水深格网数据进行试验,并与中值滤波法、趋势面滤波法进行比较。结果表明,本文方法在精度、稳定性及噪声稳健性方面有所提高,验证了本文方法在海底地形数据处理上具有可行性。     

结合深度学习和图割法的遥感影像建筑物检测

提出一种基于卷积神经网络和图割法的自动提取高分影像建筑物的方法。首先,通过卷积神经网络定位与检测建筑物的位置,逐一提取单个建筑物轮廓,利用检测结果分别建立建筑物和非建筑物的高斯混合模型（GMM）,然后结合最大流最小割的图像分割方式实现全局优化,完成建筑物初步提取,最后用形态学进行优化。通过试验证明了该方法的可行性。     

A literature synthesis of LiDAR applications in transportation: feature extraction and geometric assessments of highways

Interest in using Light Detection and Ranging (LiDAR) technology in Transportation Engineering has grown over the past decade. The high accuracy of LiDAR datasets and the efficiency by which they can be collected has led many transportation agencies to consider mobile LiDAR as an alternative to conventional tools when surveying roadway infrastructure. Nonetheless, extracting semantic information from LiDAR datasets can be extremely challenging. Although extracting roadway features from LiDAR has been considered in previous research, the extraction of some features has received more attention than others. In fact, for some roadway design elements, attempts to extract those elements from LiDAR have been extremely scarce. To document the research that has been done in this area, this paper conducts a thorough review of existing studies while also highlighting areas where more research is required. Unlike previous research, this paper includes a thorough review of the previous attempts at data extraction from LiDAR while summarizing the detailed steps of the extraction procedure proposed in each study. Moreover, the paper also identifies common tools and techniques used to extract information from LiDAR for transportation applications. The paper also highlights common limitations in existing algorithms that could be improved in future research. This paper represents a valuable resource for researchers and practitioners interested in knowing the current state of research on the applications of LiDAR in the field of Transportation Engineering while also understanding the opportunities and challenges that lie ahead.     

River centerline extraction using the multiple direction integration algorithm for mixed and pure water pixels

The river centerline is a basic hydrological characteristic. Most prior studies have used remote sensing data to extract the river centerline from the open water region in a pure water pixel region. Extracting this type of river is relatively easy. However, extracting the centerline of a micro-river, which is mainly composed of mixed water pixels, is challenging. This paper presents a novel method, called the Multiple Direction Integration Algorithm (MDIA), to extract the river centerline using an image-enhancing method combined with river morphology. MDIA can be applied to regions mainly composed of pure water pixels, as well as to regions consisting of mixed water pixels in the index image. The method first calculates the normalized difference vegetation index (NDVI) and enhances the river linear structure using a Hessian matrix. Second, a small window is constructed as a circular structural element. In the window region, the local threshold is automatically obtained using water-oriented clustering segmentation and prior river knowledge to judge the pixel type. After completing the river centerline extraction in the current window, the next detecting window is generated to continue judgment. The structural element automatically executes river centerline judgment until the entire river centerline is extracted. The Landsat 8 images of six regions with different geomorphologies were chosen to analyze the method’s performance. The test sites include high mountain region, low mountain region, plains region with farmland and a residential region. The experimental results show that the optimal threshold of the processing results ranged from 0.2 to 0.3. In this range, the user’s accuracy is 0.813 to 0.997, and the producer’s accuracy is 0.981 to 1. The MDIA effectively and correctly extracts the river network in mixed-pixel regions. The presented method provides an effective algorithm for river centerline extraction that can be used to expand and update river datasets and provide reliable river centerline data for relevant hydrology studies.     

基于面片裁切技术的点云轮廓线生成算法

随着点云在各个领域的广泛应用,对点云处理相关技术的研究更为活跃。目前,研究的热点主要集中在点云去噪、配准、分割、增强、特征提取和重建等方面。其中,特征提取起着承前启后的作用,具有十分重要的研究价值。基于面片裁切技术的点云轮廓线生成算法。首先,对点云数据进行面片提取,通过面片生长、融合生成特征面片,并根据提出的面片双向索引法快速构造特征线,最终实现点云平面轮廓线的生成。实验证明,本算法可快速、准确、有效地实现点云轮廓线的生成。     

运用人工鱼群算法的3D扫描碎片重建探究

针对传统三维碎片拼接匹配过程中依赖单一特征及存在误差累积的问题,提出了一种运用鱼群算法的全局最优匹配方法。该方法先对碎片点云数据进行多特征提取,结合纹理、专家经验信息对混合在一起的多种类型碎片进行粗糙集分类,之后采用鱼群算法的最优解求得最佳匹配方案。实例验证所提全局匹配方法具有能力强、与初始位置无关及较强的稳健性等特点,为三维碎片的全局匹配提供了一种有效的解决方案。     

一种基于室内点云数据的建筑墙线提取

针对室内点云数据无结构化属性、数据间无连接、不承载语义信息且数据点密度高的特点,结合建筑物点云几何特征和室内导航需求,通过数据降维简化建筑几何特征提取的复杂性,提出一种基于室内点云数据提取建筑物墙线的方法.该方法首先通过向特定方向投影,利用点云密度直方图完成天花板面、地板面和房间墙面的初步分割;然后将房间墙面点云数据向...     

车载LiDAR点云中的建筑物特征提取

针对车载LiDAR点云数据处理复杂、时间长的问题,本文以地物不同特征值作为建筑物自动提取算法的依据,通过点云数据预处理、聚类分析等一系列流程最终实现一般建筑物点云的自动提取.通过两个实验区点云数据的提取与相应的实际地物进行精度分析对比,结果表明本文算法对实例测区环境下的不同建筑物点云提取具有较好的有效性,满足数字城市三...     

奇异性理论在弱异常信息提取中的应用—以冀北隐伏中-酸性侵入岩体为例

冀北地区是河北省矿产资源重要聚集地之一,成矿条件优越,其中中-酸性侵入岩体与银铅锌多金属矿床成矿密切相关。然而,大量的第四系覆盖压制了一些深部及隐伏弱异常成矿信息,使得常规化探方法在该覆盖区提取效果微弱。本文运用奇异性分析及主成分分析对冀北1：20万水系沉积物常量元素化探数据进行分析处理,提取区内成矿元素弱异常,并将其与未进行奇异性处理的结果进行对比。结合区域地质背景、物化探资料圈定覆盖区内与中-酸性侵入岩体有关的异常区,对成矿作用、成矿规律进行分析、讨论,以期揭示区内元素富集规律。结果显示,上述组合方法可以有效提取元素弱异常并进行成矿有利区圈定,并且圈定位置与已知岩体位置、区域构造条件相吻合,共圈出隐伏异常点7个,推测其为成矿远景区。