应用多传感器目标跟踪融合技术实现滑坡多点监测数据综合信息的提取

目前的滑坡预报模型只能利用一个关键监测点的监测数据，而不能同时利用多个监测点的监测信息，即只能处理一维时间序列问题.为了解决这个技术难题，本文将滑坡视为一个机动目标，将对滑坡的监测视为对机动目标的跟踪，提出了利用多传感器目标跟踪融合技术来处理滑坡多个点的监测数据的方法，分析了其可行性，并用实例说明了本文提出的方法在实际应用中是有效的.     

Image processing of 2D resistivity data for imaging faults

A methodology to locate automatically limits or boundaries between different geological bodies in 2D electrical tomography is proposed, using a crest line extraction process in gradient images. This method is applied on several synthetic models and on field data set acquired on three experimental sites during the European project PALEOSIS where trenches were dug. The results presented in this work are valid for electrical tomographies data collected with a Wenner-alpha array and computed with an l₁ norm (blocky inversion) as optimization method. For the synthetic cases, three geometric contexts are modelled: a vertical and a dipping fault juxtaposing two different geological formations and a step-like structure. A superficial layer can cover each geological structure. In these three situations, the method locates the synthetic faults and layer boundaries, and determines fault displacement but with several limitations. The estimated fault positions correlate exactly with the synthetic ones if a conductive (or no superficial) layer overlies the studied structure. When a resistive layer with a thickness of 6 m covers the model, faults are positioned with a maximum error of 1 m. Moreover, when a resistive and/or a thick top layer is present, the resolution significantly decreases for the fault displacement estimation (error up to 150%). The tests with the synthetic models for surveys using the Wenner-alpha array indicate that the proposed methodology is best suited to vertical and horizontal contacts. Application of the methodology to real data sets shows that a lateral resistivity contrast of 1:5–1:10 leads to exact faults location. A fault contact with a resistivity contrast of 1:0.75 and overlaid by a resistive layer with a thickness of 1 m gives an error location ranging from 1 to 3 m. Moreover, no result is obtained for a contact with very low contrasts (1:0.85) overlaid by a resistive soil. The method shows poor results when vertical gradients are greater than horizontal ones. This kind of image processing technique should be systematically used for improving the objectiveness of tomography interpretation when looking for limits between geological objects.     

高分二号卫星影像融合方法探析

图像融合技术是卫星影像处理过程中的一个重要步骤,它可以将全色影像的高空间分辨率特点与多光谱影像的高光谱分辨率特点有机地结合起来,使得融合后的影像更利于后续解译。由于融合处理方法的原理以及获取影像的传感器特性不同,不同的卫星影像适合于不同的融合方法。针对高分二号卫星影像的特点,本文采用Gram-Schmidt融合法、FIHS变换方法、HPF方法、Pansharp方法进行试验。实验结果表明:HPF方法所得融合结果在光谱特性保持方法性能最优,而Pansharp方法融合结果在信息量的保持以及清晰度方面效果更优,因此,在实际应用中,应根据具体需求进行方法选择。     

Integrating ASTER and GLASS broadband emissivity products using a multi-resolution Kalman filter

In this study, the multi-resolution Kalman filter (MKF) algorithm, which can handle multi-resolution problems with high computational efficiency, was used to blend two emissivity products: the Global LAnd Surface Satellite (GLASS) (BBE) product and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) narrowband emissivity (NBE) product. The ASTER NBE product was first converted into a BBE product. A new detrending method was used to transfer the BBEs into a process suitable for the MKF. The new detrending method was superior to the two existing methods. Finally, both the de-trended GLASS and ASTER BBE products were incorporated into the MKF framework to obtain the optimal estimation at each scale. Field measurements collected in North America were used to validate the integrated BBEs. Visually, the fusion map showed good continuity, with the exception of the border areas, and the quality of the fusion map was better than that of the original maps. The validation results indicate that the MKF improved the BBE product accuracy at the coarse scale. In addition, the MKF was capable of recovering missing pixels at a finer scale.     

TRMM星载测雨雷达和地基雷达反射率因子数据的三维融合

TRMM卫星上的测雨雷达(TRMMPR)探测资料分布均匀且具有很高的垂直分辨率,但灵敏度较低;地基雷达(GR)水平分辨率较高且具有较高的灵敏度,但其垂直分辨率低。通过将TRMM PR与GR反射率因子数据的三维数据融合,得到了更优的反射率因子图像。测雨雷达与地基雷达三维数据融合主要分为以下几步:测雨雷达与地基雷达数据预处理——如去杂波、衰减校正;测雨雷达与地基雷达时空匹配;选取和应用合适的三维图像融合算法;对融合后的图像进行效果评估。试验结果表明:融合后的图像不仅增大了信息量,更好地检测弱降水,还提高了空间三维(3D)分辨率,能更好地反映降水区域细节,且使得数据总体上具有更高的完整性和可靠性。此外,还将基于雷达资料估测的降水数据与地面雨量计数据进行对比,估计反射率因子数据融合在降水测量上的有效性。      

Evaluation of a rule-based compositing technique for Landsat-5 TM and Landsat-7 ETM+ images

Image compositing is a multi-objective optimization process. Its goal is to produce a seamless cloud and artefact-free artificial image. This is achieved by aggregating image observations and by replacing poor and cloudy data with good observations from imagery acquired within the timeframe of interest. This compositing process aims to minimise the visual artefacts which could result from different radiometric properties, caused by atmospheric conditions, phenologic patterns and land cover changes. It has the following requirements: (1) image compositing must be cloud free, which requires the detection of clouds and shadows, and (2) the image composite must be seamless, minimizing artefacts and visible across inter image seams. This study proposes a new rule-based compositing technique (RBC) that combines the strengths of several existing methods. A quantitative and qualitative evaluation is made of the RBC technique by comparing it to the maximum NDVI (MaxNDVI), minimum red (MinRed) and maximum ratio (MaxRatio) compositing techniques. A total of 174 Landsat TM and ETM+ images, covering three study sites and three different timeframes for each site, are used in the evaluation. A new set of quantitative/qualitative evaluation techniques for compositing quality measurement was developed and showed that the RBC technique outperformed all other techniques, with MaxRatio, MaxNDVI, and MinRed techniques in order of performance from best to worst.     

Estimating winter wheat biomass by assimilating leaf area index derived from fusion of Landsat-8 and MODIS data

A sufficient number of satellite acquisitions in a growing season are essential for deriving agronomic indicators, such as green leaf area index (GLAI), to be assimilated into crop models for crop productivity estimation. However, for most high resolution orbital optical satellites, it is often difficult to obtain images frequently due to their long revisit cycles and unfavorable weather conditions. Data fusion algorithms, such as the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and the Enhanced STARFM (ESTARFM), have been developed to generate synthetic data with high spatial and temporal resolution to address this issue. In this study, we evaluated the approach of assimilating GLAI into the Simple Algorithm for Yield Estimation model (SAFY) for winter wheat biomass estimation. GLAI was estimated using the two-band Enhanced Vegetation Index (EVI2) derived from data acquired by the Operational Land Imager (OLI) onboard the Landsat-8 and a fusion dataset generated by blending the Moderate-Resolution Imaging Spectroradiometer (MODIS) data and the OLI data using the STARFM and ESTARFM models. The fusion dataset had the temporal resolution of the MODIS data and the spatial resolution of the OLI data. Key parameters of the SAFY model were optimised through assimilation of the estimated GLAI into the crop model using the Shuffled Complex Evolution-University of Arizona (SCE-UA) algorithm. A good agreement was achieved between the estimated and field measured biomass by assimilating the GLAI derived from the OLI data (GLAI_L) alone (R² = 0.77 and RMSE = 231 g m⁻²). Assimilation of GLAI derived from the fusion dataset (GLAI_F) resulted in a R² of 0.71 and RMSE of 193 g m⁻² while assimilating the combination of GLAI_L and GLAI_F led to further improvements (R² = 0.76 and RMSE = 176 g m⁻²). Our results demonstrated the potential of using the fusion algorithms to improve crop growth monitoring and crop productivity estimation when the number of high resolution remote sensing data acquisitions is limited.     

多尺度细节增强的遥感影像超分辨率重建

鉴于现有超分辨率重建方法难以突显重建影像细节信息的问题,提出多尺度细节增强的遥感影像超分辨率重建模型框架。首先,通过最小二乘滤波方法将序列影像分解成包含大尺度边缘的平滑信息和包含中小型尺度的细节信息;其次,利用插值方法得到相应的高分辨率细节信息和平滑信息,构造纹理细节增强函数,提升中小型细节的增强幅度;最终,融合细节信息和平滑信息,得到初始的超分辨率重建结果,并利用局部优化模型进一步改善重建影像质量。选取同时相和多时相遥感影像作为试验数据。试验结果表明,本文重建结果与插值方法、TV方法和MAP方法相比,在客观评价指标上均有显著提高,明显改善了重建影像的纹理细节。论文提出的多尺度细节增强的超分辨率重建方法,可以使重建影像提供更多高频细节信息,具有较好鲁棒性和普适性。     

利用多光谱卫星遥感和深度学习方法进行青藏高原积雪判识

青藏高原积雪对全球气候变化十分重要,针对已有积雪遥感判识方法中普遍采用的可见光与红外光谱数据易受复杂地形与高海拔影响,导致青藏高原地区积雪判识精度较低的问题,提出了一种基于多光谱遥感与地理信息数据特征级融合的积雪遥感判识方法:以风云三号卫星可见光与红外多光谱遥感资料与多要素地理信息作为数据源,由地面实测雪深数据与现有积雪产品交叉筛选出样本标签,构建并训练基于层叠去噪自编码器(SDAE)的特征融合与分类网络,从而有效辨识青藏高原遥感图像中的云、积雪以及无雪地表。经地面实测雪深数据验证,该方法分类精度显著高于使用相同数据源的FY-3A/MULSS积雪产品,略高于国际主流积雪产品MOD10A1与MYD10A1,并且年均云覆盖率最低。试验结果表明该方法可有效地减少云层对积雪判识的干扰,提升分类精度。     

遥感数据融合的进展与前瞻

数据融合是提升遥感影像应用能力的重要手段,一直是遥感信息处理与应用领域的研究热点。本文系统综述了遥感数据融合的进展与前瞻:首先对数据融合的层次与分类进行了总结和归纳,将遥感数据融合划分为同质遥感数据融合、异质遥感数据融合、遥感—站点数据融合、遥感—非观测数据融合4大类;在此基础上,重点针对时—空—谱光学遥感数据的融合,从多视超分辨率融合、多尺度融合、空—谱融合、时—空融合、时—空—谱一体化融合等方面进行了详细阐述;最后总结了遥感数据融合的前瞻研究方向,包括时—空—谱一体化融合的拓展、空天地观测数据的跨尺度融合、传感网环境下的在线融合、面向应用的融合方法等。