An Automatic Method for Detecting Transients and Variable Sources in AST3 Survey Based on Image Subtraction and Random Forest     

《Chinese Astronomy and Astrophysics》2020,44(1):41-60

AST3-2 (the second Antarctic Survey Telescope) is located in Antarctic Dome A, the loftiest ice dome on the Antarctic Plateau. It produces a huge amount of observational data which require a more efficient data reduction program to be developed. Also the data transmission in Antarctica is much difficult, thus it is necessary to perform data reduction and detect variable and transient sources remotely and automatically in Antarctica, but this attempt is restricted by the unsatisfactory performance of the low power consumption computer in Antarctica. For realizing this purpose, to develop a new method based on the existing image subtraction method and random forest algorithm, taking the AST3-2 2016 dataset as the test sample, becomes an alternative choice. This method performs image subtraction on the dataset, then applies the principle component analysis to extract the features of residual images. Random forest is used as a machine learning classifier, and in the test a recall rate of 97% is resulted for the positive sample. Our work has verified the feasibility and accuracy of this method, and finally found out a batch of candidates for variable stars in the AST3-2 2016 dataset.  相似文献   

Research on Parallel Algorithms for uv-faceting Imaging     

《Chinese Astronomy and Astrophysics》2019,43(3):424-443

The uv-faceting imaging is one of the widely used large field of view imaging technologies, and will be adopted for the data processing of the low-frequency array in the first stage of the Square Kilometre Array (SKA1). Due to the scale of the raw data of SKA1 is unprecedentedly large, the efficiency of data processing directly using the original uv-faceting imaging will be very low. Therefore, a uv-faceting imaging algorithm based on the MPI (Message Passing Interface)+OpenMP (Open Multi-Processing) and a uv-faceting imaging algorithm based on the MPI+CUDA (Compute Unified Device Architecture) are proposed. The most time-consuming data reading and gridding in the algorithm are optimized in parallel. The verification results show that the results of the proposed two algorithms are basically consistent with that obtained by the current mainstream data processing software CASA (Common Astronomy Software Applications), which indicates that the proposed two algorithms are basically correct. Further analysis of the accuracy and total running time shows that the MPI+CUDA method is better than the MPI+OpenMP method in both the correctness rate and running speed. The performance test results show that the proposed algorithms are effective and have certain extensibility.  相似文献   

基于卷积神经网络与条件随机场方法提取乡镇非正规固体废弃物     

刘懿兰  黄晓霞  李红旮  柳泽  陈崇  王新歌 《地球信息科学学报》2019,21(2):259-268

随着村镇经济建设发展,生活垃圾和工业固体废弃物造成的污染问题日益突出,已经成为制约新农村建设发展和生态文明建设的关键问题,而目前针对乡镇非正规固体废弃物的调查与统计主要依赖全国各乡镇相关部门逐级调查上报,工作量较大。本文基于高分辨率遥感影像,将深度学习模型和条件随机场模型相结合引入到乡镇固体废弃物的提取研究中,探索一种基于深度卷积神经网络的乡镇固体废弃物提取模型。由于固体废弃物在影像上表现为面积小,分布破碎等特点,为了提高工作效率,将模型特分为识别和提取2个部分：① 通过全连接卷积网络（CNN）对固体废弃物进行快速识别判断,筛选感兴趣区域影像块;② 在传统的全卷积神经网络(FCN)的基础上加入条件随机场模型(CRF)提取固体废弃物边界,提高整体分割精度。根据安徽、山西等地区相关部门上报固体废弃物堆放点以及住房与城乡建设部城乡规划管理中心进行野外检查的结果,实验最终识别精度达到86.87%以上;形状提取精度为89.84%,Kappa系数为0.7851,识别与提取精度均优于传统分类方法。同时,该方法已经逐步应用于住房和城乡建设部有关成都、兰州、河北等部分乡镇非正规固体废弃物的核查工作,取得了较为满意的结果。  相似文献   

基于小波变换的射频干扰消除方法的研究          下载免费PDF全文

林天琪  吴亚军  朱人杰  徐志骏 《天文学报》2021,62(3):29

在射电天文观测中,射频干扰(Radio Frequency Interference, RFI)会以多种形式混入望远镜接收系统,给观测带来误判或者降低观测信噪比.近年来国内国际射电天文快速发展,国内国际大型射电望远镜和阵列先后建设,观测灵敏度大为提高,射频干扰的影响尤为突出.随着科技发展和人类活动的加剧,射频干扰日益严重且不可逆转.提出利用2维离散小波变换的方法分析射电天文观测的数据,对望远镜系统输出的时间频率序列进行小波变换,根据小波系数分离出原始信号中各分量,每个分量统计得到相应的阈值,将各分量与阈值相比较识别干扰成分并标记去除.利用该方法对实际观测数据进行了处理,结果表明该方法能够很好地标记并消减干扰信号,且提高了观测的信噪比.  相似文献   

Research on the Application of Fast-steering Mirror in Stellar Interferometers     

Mei Rong  Hu Zhong-wen  Xu Teng  Sun Chang-sheng 《Chinese Astronomy and Astrophysics》2018,42(3):475-486

In stellar interferometers, the fast-steering mirror (FSM) is widely utilized to correct the wavefront tilt caused by the atmospheric turbulence and internal instrumental vibration, because of its high resolution and fast response frequency. In this study, the non-coplanar error between the FSM and the actuator deflection axis introduced by the manufacturing, assembly, and adjustment is analyzed systematically. Via a numerical method, the additional optical path difference (OPD) caused by the above factors is studied, and its effect on the fringe tracking accuracy of a stellar interferometer is also discussed. On the other hand, the starlight parallelism between the beams of two arms is one of the main factors for the loss of fringe visibility. By analyzing the influence of wavefront tilt caused by the atmospheric turbulence on fringe visibility, a simple and efficient real-time correction scheme of starlight parallelism is proposed based on a single array detector. The feasibility of this scheme is demonstrated by a laboratory experiment. The results show that after the correction of fast-steering mirror, the starlight parallelism meets preliminarily the requirement of a stellar interferometer on the wavefront tilt.  相似文献   

海面溢油无人机高光谱遥感检测与厚度估算方法   总被引：2，自引：0，他引：2  

任广波  过杰  马毅  罗旭东 《海洋学报》2019,41(5):146-158

海上溢油是海洋国家所面临的共同问题，但至今仍没有一种可靠实用的海上溢油准确识别和油量遥感监测方法。为此，本文以无人机高光谱遥感为手段，开展了海面溢油检测与厚度估算方法研究。实验中，通过搭建室外大型水槽溢油实验装置，获取了模拟真实海洋环境条件下不同溢油量的遥感和现场光谱数据，在此基础上，分析并提取了海上溢油特征光谱波段，给出了海上溢油高光谱检测模型；针对现场实验条件下水面油膜厚度难以测定的问题，设计了3种利用总体溢油量的油膜厚度估算模型。得到如下主要结论:（1）675 nm和699 nm是海上溢油检测的有效特征波段，但对极薄的油膜没有检测能力；（2）提出了归一化溢油指数模型、反比例模型和吸收基线模型等3种海上溢油油膜厚度估算模型，其中对于薄油膜（厚度≤ 5 μm）和厚油膜（厚度>50 μm），反比例模型是溢油厚度反演的首选也是唯一选择。对于中厚度油膜，晴朗天气条件下，归一化溢油指数模型是油膜厚度反演的首选，同时反比例模型和溢油吸收基线模型也都有较好的反演能力，而在多云天气条件下，反比例模型效果最佳。  相似文献   

顾及地形起伏的无人机影像覆盖分析方法     

张衡  李骁  叶朋飞 《测绘通报》2020,(1):102-106

使用无人机实施测绘航空摄影时，由于无人机相对航高较低，地面起伏会对无人机影像的分辨率、覆盖范围、重叠度造成较大的影响，影像成果会出现分辨率不足、重叠度不够、覆盖漏洞等缺陷。针对这一情况，本文提出了一种利用数字微分正解法的计算方法，借助DEM准确计算每张影像的覆盖范围，并使用FME软件高效生成全部影像的覆盖范围。经过实际使用，验证了该方法可以在航线设计阶段准确预测并分析全部影像的覆盖范围、重叠度，因此可及时发现设计问题并调整航线。该方法可以有效减少因地形起伏造成的影像覆盖缺陷，减少返工现象，从整体上提高了作业效率。  相似文献   

波段选择协同表达高光谱异常探测算法     

朱德辉  杜博  张良培 《遥感学报》2020,24(4):427-438

高光谱遥感影像具有光谱分辨率极高的特点,承载了大量可区分不同类型地物的诊断性光谱信息以及区分亚类相似地物之间细微差别的光谱信息,在目标探测领域具有独特的优势。与此同时,高光谱遥感影像也带来了数据维数高、邻近波段之间存在大量冗余信息的问题,高维度的数据结构往往使得高光谱影像异常目标类和背景类之间的可分性降低。为了缓解上述问题,本文提出了一种基于波段选择的协同表达高光谱异常探测算法。首先,使用最优聚类框架对高光谱波段进行选择,获得一组波段子集来表示原有的全部波段,使得高光谱影像异常目标类与背景类之间的可分性增强。然后使用协同表达对影像上的像元进行重建,由于异常目标类和背景类之间的可分性增强,对异常目标像元进行协同表达时将会得到更大的残差,异常目标像元的输出值增大,可以更好地实现异常目标和背景类的分离。本文使用了3组高光谱影像数据进行异常目标探测实验,实验结果表明,该方法与其他现有高光谱异常目标探测算法对比,曲线下面积AUC(Area Under Curve)值更高,可以更好地实现异常目标与背景分离,能够更有效地对高光谱影像进行异常目标探测。  相似文献   

结合图像增强和恢复的增强现实跟踪注册     

郭士祥  范铀  范冲 《测绘通报》2020,(8):39

增强现实中的跟踪注册技术一直是研究的重点和难点，而地下矿道和巷道内亮度低，产生的图像较昏暗，车载相机快速运动和抖动，对传统的基于特征匹配的跟踪注册提出了挑战。本文从提高增强现实中跟踪注册稳健性和精度出发，采用基于Retinex改进的方法增强昏暗图像的亮度，同时利用基于对抗神经网络的方法恢复运动模糊图像。首先提取图像ORB特征，实现初始化；然后根据跟踪特征点的数量，开启图像增强和图像恢复线程，提高特征点提取质量和数量。在数据集和真实模拟场景下的试验结果显示，跟踪精度提高了12%左右，在低亮度和含有轻微模糊的情况下，跟踪注册稳健性也有显著提高。  相似文献   

基于深度学习理论的无人机影像分类     

阳成 《北京测绘》2020,(4):481-484

针对无人机影像深度学习分类方法缺乏现状,本文利用深度学习理论卷积神经网络方法对无人机影像进行了分类。该法首先抽取无人机影像作为训练集和检验集,然后建立一个2个卷积层-池化层的卷积神经网络模型进行深度学习,通过设定参数并运行模型实现无人机影像分类。实验表明,本文提出的方法可完成较复杂地区无人机影像分类,其分类精度与支持向量机方法相当,为无人机遥感影像分类提供了一个崭新的技术视点。  相似文献