一种拓展的半物理时空融合算法及其初步应用

Landsat 5卫星较低的时间分辨率(16天)使得其很难获得大区域的、时相一致的清晰影像数据集。本文发展了一种基于半物理模型的时空融合算法-即乘性调制融合算法,并借助多时序的MODIS反射率数据来生成多时相的Landsat TM/ETM+反射率合成影像,经镶嵌后得到区域尺度的高时空分辨率地表反射率数据集(Landsat TM/ETM+)。本文利用吉林省2006年—2011年的Landsat 5 TM地表反射率数据以及500 m的MOD09A1反射率产品来生成3个时相的Landsat 5 TM反射率合成数据,从而获得研究区在上述时相下地表反射率数据的镶嵌图。初步分析表明,所生成的Landsat 5 TM反射率数据的光谱分布特征与MOD09A1反射率数据较为一致,且图像在整体上光谱特征的连续性较好。     

电感耦合等离子体发射光谱法同时测定锡矿石中锡钨钼铜铅锌

锡矿石是难分解的矿物,共生与伴生元素多,其中的锡钨钼在单一盐酸溶液中易沉淀,准确测定锡矿石中的主次量元素一直是分析技术难点。本文以过氧化钠为熔剂,高温熔融样品,在酒石酸-盐酸-双氧水体系中进行酸化,选用该矿种中仅含有少量的钴作为内标,建立了电感耦合等离子体发射光谱同时测定锡矿石中锡钨钼铜铅锌的分析方法。方法线性范围为0.00~40.0 mg/L;方法检出限为锡10 mg/kg,钨30 mg/kg,钼3.3 mg/kg,铜12 mg/kg,铅15 mg/kg,锌40 mg/kg;方法精密度(n = 9)小于5.0%,实际样品的测定值与传统化学方法及国家标准方法的测定值吻合较好。本方法采用过氧化钠碱熔锡矿石,溶样彻底,并省去了氢氟酸挥发硅的蒸酸过程,节约了样品处理时间;采用酒石酸-双氧水-盐酸体系溶解熔融物,有利于溶液中的锡钨钼形成稳定的络合物,避免了单纯盐酸体系下产生钨酸、钼酸和锡酸沉淀导致测定结果偏低的问题。     

蓝晶石矿中氟钠镁铝硅铁钛钾钙元素的X射线荧光光谱分析

蓝晶石的分析通常采用碱熔体系,重量、容量、比色、原子吸收光谱法、离子选择电极法等多种分析手段进行单独测定,对含有刚玉、金红石矿物的难熔性蓝晶石样品,这种分析方法常因熔矿不完全而导致测定结果偏低,而且分析手续冗长,操作复杂,不能满足地质测试的需要。本文采用玻璃熔融法制样,建立了X射线荧光光谱法同时测定蓝晶石矿中F、Na、Mg、Al、Si、Fe、Ti等主量元素的分析方法。以不同矿种的标准物质和自制含多种矿物组分的蓝晶石管理样拟合校准曲线,对玻璃熔融法的稀释比、熔矿温度及对F元素的影响因素等测定条件进行优化,确定选择样品与四硼酸锂-偏硼酸锂混合熔剂的稀释比为1:10,在1050℃温度下实际样品熔矿完全,各元素的分析结果与化学分析法的测定值基本吻合。方法检出限小于0.05%,方法精密度(RSD, n=7)小于4.5%。本法操作简单,重现性好,准确可靠,解决了难熔样品的熔矿问题,同时也很好地解决了传统方法费时、耗材、不能同时测定多元素的问题。     

电感耦合等离子体发射光谱法测定锆钛砂矿中铪钛锆

锆钛砂矿是一种极难消解的矿物,除氢氟酸外,几乎不溶于所有的酸,由于矿物中铪、钛、锆含量高,而铪、钛、锆又易水解形成难溶的偏铪酸、偏钛酸、偏锆酸析出,样品前处理给定量分析带来很大困难。传统的化学法繁琐费时,且只能进行锆(铪)合量的分析。本文建立了电感耦合等离子体发射光谱法(ICP-AES)测定锆钛砂矿样品中铪、钛、锆的方法。通过筛选四种溶矿方法,确定在刚玉坩埚中用过氧化钠于700℃时熔融样品,硝酸-EDTA浸取盐分前处理矿物,利用EDTA的强络合性质可使铪、钛、锆形成稳定的可溶络合物,制备出有代表性的样品溶液;在ICP-AES分析中,采用Re作为内标和大的高频功率消除了基体效应的影响。方法的精密度(RSD,n=11)低于1.3%,Hf、Ti、Zr的检出限分别为0.97 μg/g、0.86 μg/g、0.33 μg/g。实际样品的测定值与化学分析方法和X射线荧光光谱法的测定结果基本吻合。。本方法采用刚玉坩埚熔矿,提高了样品处理数量,降低了分析成本,适用于难熔锆钛砂矿样品的快速定量分析。     

Ground peak identification in dense shrub areas using large footprint waveform LiDAR and Landsat images

Large footprint waveform LiDAR data have been widely used to extract tree heights. These heights are typically estimated by subtracting the top height from the ground. Compared to the top height detection, the identification of the ground peak in a waveform is more challenging. This is particularly evident in ground detection in shrub areas, where the reflection of the shrub canopy may significantly overlap with the ground reflection. To tackle this problem, a novel method based on Partial Curve-Fitting (PCF) of the shrub peak was developed to detect the ground peak. Results indicated that the PCF method improves ground identification by 32–42%, compared to existing methods. To offer further improvement, a Multi-Algorithm Integration Classifier (MAIC) was built to fuse multiple ground peak algorithms and selectively apply the best method for each waveform plot. The PCF ground peak identification method along with the MAIC-based fusion is expected to significantly improve ground detection and shrub height estimation, thus assisting biodiversity, forest succession, and carbon sequestration studies, while offering an early example of future multiple algorithm integration.     

Land-surface temperature retrieval at high spatial and temporal resolutions based on multi-sensor fusion

Land-surface temperature (LST) is of great significance for the estimation of radiation and energy budgets associated with land-surface processes. However, the available satellite LST products have either low spatial resolution or low temporal resolution, which constrains their potential applications. This paper proposes a spatiotemporal fusion method for retrieving LST at high spatial and temporal resolutions. One important characteristic of the proposed method is the consideration of the sensor observation differences between different land-cover types. The other main contribution is that the spatial correlations between different pixels are effectively considered by the use of a variation-based model. The method was tested and assessed quantitatively using the different sensors of Landsat TM/ETM+, moderate resolution imaging spectroradiometer and the geostationary operational environmental satellite imager. The validation results indicate that the proposed multisensor fusion method is accurate to about 2.5 K.     

基于多特征融合与典型降维方法的高光谱影像分类

高光谱影像的冗余信息给影像的分类效果带来一定的负面影响。本文利用CB法(CfsSubsetEval评估器结合Best-First搜索策略)与PCA变换两种降维方法,分别结合随机森林分类器对4种多特征融合方案(共8种组合)进行高光谱影像分类对比,基于分类的总体精度、Kappa系数探究提高高光谱影像分类的最佳组合方法。结果表明:①多特征融合可提升高光谱影像的分类效果,两种降维方法的分类精度均随地理特征、纹理特征、指数特征的加入而逐渐提高。②两种降维方法中,经CB法降维后的分类精度均比通过PCA变换降维的分类精度高。在构造的8种组合中,基于所有特征信息(光谱特征、地理特征、纹理特征、指数特征)的CB法分类精度最高,其总体精度为98.01%;Kappa系数为0.969 9。     

实景三维多源数据场景融合北大核心CSCD

在城市级实景三维建设中,对多来源、多尺度实景三维数据进行场景融合,能够进一步完善实景三维场景细节表达,提高城市级实景三维场景的完整性,满足实景三维场景局部更新机制要求。本文针对海量、异构、多尺度实景三维融合技术方法开展了相关研究,探索并改进了多源实景三维数据的空间精度匹配与数据接边的方法,结合实景三维数据成果的标准化、轻量化处理方法,提升了实景三维场景调度显示效率,进一步扩大了实景三维融合成果的应用领域。     

融合多源测量数据的桥梁挠度异常探测方法

桥梁是一种重要的交通基础设施,保障了人员和物资的流动运输,桥梁安全状态监测至关重要。受桥梁自身承重、桥面移动荷载和环境温度等因素的影响,桥梁挠度不断发生变化,而挠度异常可能会引起桥梁的结构性损伤而产生安全隐患。针对目前挠度异常探测方法没有综合考虑其外部环境和挠度自身变化特征的不足,本文提出了一种融合多源测量数据的桥梁挠度异常探测方法。利用环境温度、桥面移动荷载及桥梁挠度计算多源测量数据特征并融合,通过随机森林分类模型识别异常挠度。试验结果表明,本文方法的异常探测精度达到88.18%,效果良好,并且优于其他典型机器学习模型,能够帮助桥梁管理部门及时发现桥梁挠度异常情况,从而提高桥梁维护管理水平。     

Detail focused fusion of hyperspectral and multispectral imagesEI北大核心CSCD

Hyperspectral image (HSI) and multispectral image (MSI) are two types of images widely used in the field of remote sensing. These images are useful in certain applications, such as environmental monitoring, target detection, and mineral exploration. HSI contains a large amount of spectral information. Photons are typically collected in a larger spatial area on the sensor to ensure a sufficiently high signal-to-noise ratio (SNR). Accordingly, the HSI spatial resolution is much lower compared with MSI. This low spatial resolution greatly affects the practicality of HSI. Accordingly, fusing a low-spatial resolution HSI (LR-HSI) with a high-spatial resolution MSI (HR-MSI) in the same scene to obtain a high-resolution HSI (HR-HSI) is a method for solving such problems, which resolves the contradiction that the spatial resolution and the spectral resolution cannot simultaneously maintain a high level. From the analysis of fusion effect, the spatial and spectral reconstruction errors of the existing algorithms are mainly reflected in the edge and detail areas. The method proposed in this work was a fusion algorithm for dictionary construction and image reconstruction based on detail attention. In terms of maintaining spectral characteristics, the spectral distribution in the detail area is complex and diverse because of the proximity effect of the image. This work proposes to perform dictionary learning on the image and detail layers. The detail perception error terms and a constraint of edge adaptive directional total variation are proposed for spatial characteristic enhancement, which is combined with a local low rank constraint in the same fusion framework to estimate the sparse coefficient. Experiments were conducted on two datasets, namely, Pavia University and Indian Pine, to verify the effectiveness of the proposed method. The quantitative evaluation metrics contain peak SNR, relative dimensionless global error in synthesis, spectral angle map, and universal image quality index. Based on the experimental comparison, the fusion result of the algorithm proposed in this work is significantly improved compared with those of the other algorithms in terms of spatial and spectral characteristics. This work uses dictionary learning to propose a fusion algorithm for dictionary construction and image reconstruction with attention to details through the analysis of the existing hyperspectral and multispectral image fusion algorithms. A hierarchical dictionary learning algorithm is proposed to address the problem of large reconstruction error in the detail part of the existing algorithms. The detail perception error term and the direction adaptive full variational regularization term are used to improve the spectral dictionary solution and coefficient estimation, respectively. The result of the fusion is the error in the spectral characteristics and spatial texture of the detail, which achieves an accurate representation of the edge detail. © 2022 National Remote Sensing Bulletin. All rights reserved.