结合正则化K-SVD和Hampel滤波的探地雷达数据重建

为减弱因地形起伏造成的探地雷达数据间的能量差异,保证探地雷达图像解译和识别的准确性,本文提出了一种正则化K-SVD字典学习和Hampel滤波算法相结合的探地雷达数据重建方法。试验采用正则化K-SVD字典学习对探地雷达信号进行能量均衡,利用Hampel滤波算法剔除均衡后的信号异常值,并对均衡后的信号进行二维可视化,从而完成探地雷达图像重建。对比试验表明,本文方法不但可以均衡原始的探地雷达信号,而且其均衡后的信号更加符合探地雷达信号传播规律,可以保证单道数据信号的质量;其重建的图像效果更好,在探地雷达图像重建方面具有较好的实用价值。     

浅淡地质雷达研发中的几个细节

为解决国内日益增长的地质雷达需求和目前国内所用雷达绝大部分都是国外进口的矛盾,作者同国内专家共同研发国产地质雷达.本文根据实际工作中的需求,探讨了地质雷达硬件和软件研发设计中的几个细节问题,使得生产出来的地质雷达能够更优质、高效的为生产服务.     

植被的微波散射与吸收特征

被动微波遥感中大尺度像元内植被种类多种多样并且分布不均匀，较低频率下像元尺度内的植被影响可以根据单个散射体的散射与衰减特性获得。本文讨论了任意朝向的各类植被散射体的计算问题。其中叶片和针叶的散射采用了一般化Rayleigh-Gans计算；杆的散射采用了无限长近似理论。关于各类散射体的衰减，本文比较了散射系数和吸收系数之和，与前向散射定理的结果，并对前向定理的适用性进行了讨论。     

车载探地雷达地下目标实时探测法

中国的城镇化加快了地下空间大规模开发与利用的进程。摸清地下空间目标的分布状况,保障城市可持续发展和地下空间资源的永久利用,是维护未来城市安全中的重要任务。探地雷达(GPR)凭借其数据采集速度快、成像分辨率高、无损检测等优点在地下空间资源调查中得以广泛应用。但仍存在GPR数据地下目标识别不准确、自动化程度低等缺陷,自动检测GPR数据中的地下目标或目标缺陷仍然是一个亟待解决的难题。为此,本文分析并确定了GPR影像中可进行识别的城市道路地下空间的7类典型目标(如雨水井、电缆等)。并根据其反射信号特征,标记了GSSI SIR30设备以400 MHz波段采集的GPR数据中的典型地下目标,构建了GPR地下目标样本库,共包含7类总数为3033个。通过迁移学习的方法,精调预训练后的Darknet53网络参数,通过端到端的YOLOV3检测方法完成地下目标的自动识别与定位。最后,利用深圳福田区彩田路GSSI SIR30装备以400 MHz波段采集的GPR数据进行试验验证。试验结果表明,本文提出的基于深度学习的地下目标探测方法对城市典型地下目标的检测精度和召回率达到85%以上,检测速度达到了16帧/s,能够有效探测GPR数据中的城市地下目标。     

GNSS-R海面目标成像仿真方法研究

结合物理光学法及传统复合电磁散射计算模型中的"四路径"方法,以GNSS信号为信号源,提出了一种基于高频散射理论的GNSS-SAR成像模型,建模过程考虑了多路径散射、遮蔽、漫散射等效应,较好地描述了电磁散射效应对海面场景GNSS-SAR成像的影响。根据电磁散射效应,分析了海面风速对成像效果产生的干扰情况,发现在本文仿真场景下,9 m/s风速海背景下的目标能够被识别出来,17 m/s风速海背景下的目标几乎淹没在海杂波中。     

一种WiFi距离交会加权融合定位算法

基于距离交会的WIFI室内定位算法受环境的影响,距离反演精度较低导致定位误差较大,不同距离反演模型对定位误差影响特性存在差异。考虑到GPR距离反演模型过度平滑引起定位结果扎堆的缺陷和传统损耗模型反演精度过低引起定位结果误差较大的缺陷,提出一种基于GPR预测模型和损耗模型的加权融合定位算法。实验结果表明:该融合算法能够有效提高定位精度并明显改善基于GPR模型定位算法的定位结果扎堆现象。     

基于多次散射的植被-土壤二向反射模型

运算过于复杂是目前多数植被二向反射模型存在的一个突出问题。对冠层的各次散射、透射和地面土壤反射分别进行分析和考虑 ,建立各次反射光谱分量与叶面积指数的函数关系。在Verstraete (1 986 )的物理模型的基础上加上多次散射和土壤反射的分量 ,建立了基于多次散射的植被 土壤二向反射模型 ,并证明仅考虑前三次散射即可满足一般精度要求。该模型克服了原模型的不足 ,并可根据叶面积指数等参量直接计算二向反射率 ,避免了运算量巨大的叠代运算 ,故便于应用。实验证明 ,该模型具有较高的精度     

Electromagnetic Scattering by Anisotropic Inhomogeneous Impedance Cylinder of Arbitrary Shape Using Physical Optics

In this letter, electromagnetic scattering from inhomogeneous anisotropic impedance cylinder of arbitrary shape is investigated by physical optics (PO) method. After the expressions of induced electric and magnetic currents on the illuminated region of surface are expressed by using PO approximation, scattered field is calculated using Huygens-type Green's formula. Obtained scattering width results are compared with those obtained by numerical methods such as NystrÖm and Method of Moments (MoM), and good agreements are observed particularly in monostatic case. Due to the fact that PO method does not need matrix inversion which requires high CPU time, it also significantly reduces computer calculation time in comparison to NystrÖm and MoM.      

Gaussian processes uncertainty estimates in experimental Sentinel-2 LAI and leaf chlorophyll content retrieval

ESA’s upcoming Sentinel-2 (S2) Multispectral Instrument (MSI) foresees to provide continuity to land monitoring services by relying on optical payload with visible, near infrared and shortwave infrared sensors with high spectral, spatial and temporal resolution. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods, which ideally should provide uncertainty intervals for the predictions. Statistical learning regression algorithms are powerful candidats for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting. In this paper, we focus on a new emerging technique in the field of Bayesian nonparametric modeling. We exploit Gaussian process regression (GPR) for retrieval, which is an accurate method that also provides uncertainty intervals along with the mean estimates. This distinct feature is not shared by other machine learning approaches. In view of implementing the regressor into operational monitoring applications, here the portability of locally trained GPR models was evaluated. Experimental data came from the ESA-led field campaign SPARC (Barrax, Spain). For various simulated S2 configurations (S2-10m, S2-20m and S2-60m) two important biophysical parameters were estimated: leaf chlorophyll content (LCC) and leaf area index (LAI). Local evaluation of an extended training dataset with more variation over bare soil sites led to improved LCC and LAI mapping with reduced uncertainties. GPR reached the 10% precision required by end users, with for LCC a NRMSE of 3.5–9.2% (r²: 0.95–0.99) and for LAI a NRMSE of 6.5–7.3% (r²: 0.95–0.96). The developed GPR models were subsequently applied to simulated Sentinel images over various sites. The associated uncertainty maps proved to be a good indicator for evaluating the robustness of the retrieval performance. The generally low uncertainty intervals over vegetated surfaces suggest that the locally trained GPR models are portable to other sites and conditions.     

临边遥感大气辐射传输的宽光谱快速计算模型

为解决目前临边遥感大气辐射传输模型存在计算光谱窄和计算速度慢等问题,基于HITRAN2008数据库、带模式算法和临边辐射传输方程建立了0.2—20μm波段大气临边辐射传输的宽光谱快速计算模型FALTRAN。建立了含有散射辐射和热辐射的临边辐射传输方程,并依据临边几何特征提出了一种基于有限差分的半球辐射累加HRA解算方法。利用FALTRAN计算分析了几个常用的遥感波段内典型大气条件和切向高度时大气的临边辐射特性,并定量分析了左、右半球辐射对总辐射的贡献情况。模型校验结果表明,FALTRAN与CDI模型的计算结果相对差异在2%以内,并与MIPAS的测量结果符合很好,验证了FALTRAN在临边遥感中大气传输计算的可靠性。     

Nondestructive Sensor Using Microwaves From Laser Plasma by Subnanosecond Laser Pulses

Conventional ground-penetrating radar (GPR) requires large-aperture antennas or long-span measurements to survey a remote location precisely. We propose a laser-driven GPR (LGPR) as a new detection method. LGPR uses microwaves from laser-produced plasmas as remote transmitters and can survey a remote location using a compact instrument. We performed numerical simulations to investigate the radiation mechanism of microwaves from laser plasmas and confirmed the pulsewidth of the laser suitable for LGPR. Experiments with subnanosecond pulse lasers clarified the feasibility and detection performance of LGPR.     

CMP Antenna Array GPR and Signal-to-Clutter Ratio Improvement

Ground-penetrating radar (GPR) is recognized as a promising sensor for detecting buried landmines. In this case, the GPR antenna(s) must be elevated above the ground. However, this requirement results in heavy surface clutter. It is therefore necessary to overcome the effect. A commonly used procedure of time gating and background averaging cannot suit to small shallow nonmetallic landmine beneath a rough ground surface. In this letter, we proposed techniques to enhance the target signal through common midpoint (CMP) antenna array and data processing techniques, including velocity spectrum and CMP multifold stacking. The method has been tested using experiment data over a rough ground under which small plastic antipersonnel landmines is shallowly buried. The result shows the signal-to-clutter ratio was dramatically improved.     

面向LAI 反演的参数化SAILH 模型

建立了一种基于植被冠层辐射传输模型SAILH 的参数化模型。该模型首先对SAILH 模型中用到的9 个 中间变量的计算过程进行简化, 然后用一个明确的表达式计算光照冠层的单次散射贡献。分别用模拟数据和2008 年黑河地区星-机-地同步实验中获取的地面测量数据对该参数化模型的反演精度和效率进行了评价。评价结果表明 该参数化模型能在保证反演精度的基础上极大的提高反演效率; 利用模拟数据进行的模型稳定性评价表明, 参数 化模型的稳定性优于SAILH 模型。     

冲击型探地雷达回波信号的等效采样方法研究

提出了一种基于等效时间采样方法的冲击型探地雷达回波信号高速采样系统，设计实现了等效时间采样的关键电路，包括步进采样脉冲发生器、采样门电路。实验证明了使用该方法采样回波信号的有效性。     

Estimation of glacier depth and moraine cover study using ground penetrating radar (GPR) in the Himalayan region

Himalayan region has one of the largest concentrations of mountain glaciers whose areal extent is changing due to global warming. In order to assess future changes in glacier extent due to global warming, information about glacier depth and debris cover is important. In this paper, application of ground penetrating radar (GPR) is discussed to assess glacier depth and debris cover. This investigation was carried out at Patseo and Samudratapu glaciers in Himachal Pradesh (West Himalaya). Antennas of frequency 50 and 100 MHz have been used for glacier depth and 500 MHz for moraine depth estimation. GPR signatures of glaciers were collected and further analyzed using velocities of electromagnetic waves in different media. The depth of Patseo glacier was estimated as 40 m. However, depth of the larger Samudra Tapu glacier could not be estimated using 50 and 100 MHz antennas. The depth of moraines was estimated using 500 MHz antenna and it varies from 0.35 cm to 0.85 cm for medial and around 1–2 m for lateral moraine at the experimental site.     

基于高斯过程回归的变形智能预测模型及应用

岩体或建构筑物的变形通常具有复杂性和非线性等特性,一般的回归模型难以精确地进行回归预测,应用高斯过程回归理论对变形监测数据呈现出的非线性特征进行时间序列分析。考虑到监测数据的不断更新和累积,以及超参数与样本集的适应性,首先研究了“递进-截尾式”超参数自动更新模式和训练样本集的选择方法;在此基础上构建了以时间作为输入项的高斯过程回归变形智能预测模型（GPR-TIPM）;将该模型应用于矿山边坡监测点非线性时间序列分析中,通过分析变形趋势,最终采用Matérn 32和平方指数协方差函数相加的方式进行核函数组合。实验结果表明,采用组合核函数的预测性能较单一核函数有所改善,该方法提高了模型的泛化能力,GPR-TIPM模型在短期内的预测效果较理想。     

Measurement of soil water content using ground-penetrating radar: a review of current methods

Soil water content (SWC) is a crucial parameter in ecology, agriculture, hydrology, and engineering studies. Research on non-invasive monitoring of SWC has been a long-lasting topic in these fields. Ground penetrating radar (GPR), a non-destructive geophysical technique, has the advantages of high resolution, deep detection depth, and high efficiency in SWC measurements at medium scale. It has been successfully applied in field investigations. This paper summarizes the recent progress in developing GPR-based SWC measurement methods, including reflected wave, ground wave, surface reflection, borehole GPR, full waveform inversion, average envelope amplitude, and frequency shift methods. The principles, advantages, limitations, and applications of these methods are described in detail. A comprehensive technical framework, which comprises the seven methods, is proposed to understand their principles and applicability. Two key procedures, namely, data acquisition and data processing, are emphasized as crucial to method applications. The suitable methods that will satisfy diverse application demands and field conditions are recommended. Future development, potential applications, and advances in hardware and data processing techniques are also highlighted.     

合成孔径雷达图像的近岸海面风场反演

根据海面微波散射原理，给出了星载合成孔径雷达(Synthetic Aperture Radar,SAR)图像海面风场反演的方法。该方法包括SAR图像辐射定标、海面风向确定和风速计算。利用该方法由加拿大RadarsatSAR图像反演获得了中国海南省东南部近岸海区海面风场，并与预报风场作了比较。结果显示，二者相符得很好，风速的均方根误差为0.94m/s，风向的均方根误差为4.03°。     

Application of machine learning techniques in groundwater potential mapping along the west coast of India

ABSTRACT

Groundwater potential mapping (GWPM) in the coastal zone is crucial for the planning and development of society and the environment. The current study is aimed to map the groundwater potential zones of Sindhudurg coastal stretch on the west coast of India, using three machine learning models: random forest (RF), boosted regression tree (BRT), and the ensemble of RF and support vector machine (SVM). In order to achieve the objective, 15 groundwater influencing factors including elevation, slope, aspect, slope length (LS), profile curvature, plan curvature, topographical wetness index (TWI), distance from streams, distance from lineaments, lithology, geomorphology, soil, land use, normalized difference vegetation index (NDVI), and rainfall were considered for inter-thematic correlations and overlaid with spring and well occurrences in a spatial database. A total of 165 spring and well locations were identified, which had been divided into two classes: training and validation, at the ratio of 70:30, respectively. The RF, BRT, and RF-SVM ensemble models have been applied to delineate the groundwater potential zones and categorized into five classes, namely very high, high, moderate, low, and very low. RF, BRT, and ensemble model results showed that 33.3%, 35.6%, and 36.8% of the research area had a very high groundwater potential zone. These models were validated with area under the receiver operating characteristics (AUROC) curve. The accuracy of RF (94%) and hybrid model (93.4%) was more efficient than BRT (89.8%) model. In order to further evaluate and validate, four different sites were subsequently chosen, and we obtained similar results, ensuring the validity of the applied models. Additionally, ground-penetrating radar (GPR) technique was applied to predict the groundwater table and validated by measured wells. The mean difference between measured and GPR predicted groundwater table was 14 cm, which reflected the importance of GPR to guide the location of new wells in the study region. The outcomes of the study will help the decision-makers, government agencies, and private sectors for sustainable planning of groundwater in the area. Overall, the present study provides a comprehensive high-precision machine learning and GPR-based groundwater potential mapping.     

一种结合Freeman分解和散射熵的MRF多极化SAR影像分割算法

针对多极化SAR图像,采用Freeman分解理论,将其分为表面散射、偶次散射、体散射、混合散射4种散射机制,并通过H/Alpha分解提取散射熵,将地物初始分为12类,并运用聚合的层次聚类算法对初始分类结果进行合并。利用Wishart分布对特征场进行建模,用模拟退火优化方法求取基于最大后验准则下的分割结果。