用于遥感图像纹理分析的子空间法与纹理变换法

本文着重探讨了纹理特征引入遥感图像分析所面临的二个问题—冗余特征压缩与纹理分割。针对第一个问题,本文利用了与传统方法迥然不同的子空间法,该法与马氏距离法比较得出,无论是效率还是精度,子空间法均优于马氏最小距离法。针对第二个问题,本文在前人工作的基础上,提出了纹理能量变换法,实验结果表明,该法用于纹理分割是可行的。     

Cloud removal for remotely sensed images by similar pixel replacement guided with a spatio-temporal MRF model

Cloud cover is generally present in remotely sensed images, which limits the potential of the images for ground information extraction. Therefore, removing the clouds and recovering the ground information for the cloud-contaminated images is often necessary in many applications. In this paper, an effective method based on similar pixel replacement is developed to solve this task. A missing pixel is filled using an appropriate similar pixel within the remaining region of the target image. A multitemporal image is used as the guidance to locate the similar pixels. A pixel-offset based spatio-temporal Markov random fields (MRF) global function is built to find the most suitable similar pixel. The proposed method was tested on MODIS and Landsat images and their land surface temperature products, and the experiments verify that the proposed method can achieve highly accurate results and is effective at dealing with the obvious atmospheric and seasonal differences between multitemporal images.     

Measures of spatio-temporal accuracy for time series land cover data

Remote sensing is a useful tool for monitoring changes in land cover over time. The accuracy of such time-series analyses has hitherto only been assessed using confusion matrices. The matrix allows global measures of user, producer and overall accuracies to be generated, but lacks consideration of any spatial aspects of accuracy. It is well known that land cover errors are typically spatially auto-correlated and can have a distinct spatial distribution. As yet little work has considered the temporal dimension and investigated the persistence or errors in both geographic and temporal dimensions. Spatio-temporal errors can have a profound impact on both change detection and on environmental monitoring and modelling activities using land cover data. This study investigated methods for describing the spatio-temporal characteristics of classification accuracy. Annual thematic maps were created using a random forest classification of MODIS data over the Jakarta metropolitan areas for the period of 2001–2013. A logistic geographically weighted model was used to estimate annual spatial measures of user, producer and overall accuracies. A principal component analysis was then used to extract summaries of the multi-temporal accuracy. The results showed how the spatial distribution of user and producer accuracy varied over space and time, and overall spatial variance was confirmed by the principal component analysis. The results indicated that areas of homogeneous land cover were mapped with relatively high accuracy and low variability, and areas of mixed land cover with the opposite characteristics. A multi-temporal spatial approach to accuracy is shown to provide more informative measures of accuracy, allowing map producers and users to evaluate time series thematic maps more comprehensively than a standard confusion matrix approach. The need to identify suitable properties for a temporal kernel are discussed.     

Evaluating geo-located Twitter data as a control layer for areal interpolation of population

Control data are critical for improving areal interpolation results. Remotely sensed imagery, road network, and parcels are the three most commonly used ancillary data for areal interpolation of population. Meanwhile, the open access geographic data generated by social networks is emerging as an alternative control data that can be related to the distribution of population. This study evaluates the effectiveness of geo-located night-time tweets data as ancillary information and its combination with the three commonly used ancillary datasets in intelligent areal interpolation. Due to the skewed Twitter user age, the other purpose of this study is to test the effect of age bias control data on estimation of different age group populations. Results suggest that geo-located tweets as single control data does not perform as well as the three other control layers for total population and all age-specific population groups. However, the noticeable enhancement effect of Twitter data on other control data, especially for age groups with a high percentage of Twitter users, suggests that it helps to better reflect population distribution by increasing variation in densities within a residential area delineated by other control data.     

高精度轻小型航空遥感系统集成技术与方法

本文是国家高技术研究发展计划(863计划)重点项目"高精度轻小型航空遥感系统核心技术及产品"的部分研究内容,着重论述基于国产轻型组合宽角数字相机、轻小型机载激光雷达(LiDAR)、高精度与小型化POS及稳定平台、高效快速数据处理系统四类核心产品,突破轻小型航空遥感载荷,满足1:500测图精度要求集成高精度轻小型航空遥感...     

全球植被叶面积指数对温度和降水的响应研究

利用一套新的遥感信息反演的叶面积指数(LAI)数据和生物气候数据,建立了全球尺度的LAI与降水及温度的总体相关和距平相关,用以揭示全球尺度的植被季节和年际的变化对气候变化的响应特征.结果发现,全球尺度植被与气候因子的季节和年际变化随不同的生态系统差异明显.植被LAI与温度的总体正相关的最大值出现在北半球的中高纬度地区;LAI与降水的总体正相关高值(＞0.8)出现在亚洲东部、北美洲北部腹地和热带非洲北部的Sahel地区;最大的LAI与温度的正距平相关(0.4-0.6)出现在东南亚南部、非洲Sahel地区的南部和巴西东部等热带地区;而从LAI与降水的距平相关来看,最明显的特征是出现在西伯利亚、亚洲北部和北美西北部的强的负距平相关.本文进一步阐明了出现相关特征差异的陆-气过程物理机制.     

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The motion of a deep-sea remotely operated vehicle system: Part 1: Motion observations

Rapid and high-resolution motion and tension measurements were made of a caged deep-sea remotely operated vehicle (ROV) system. Simultaneous measurements were made of all six components of motion at the cage and ship A-frame and of the tension in the tether at the ship. Data were collected for cage depths of 0–1765 m. The most significant forcing was in the wave-frequency band (0.1–0.25 Hz) and accounted for over 90% of the variance of vertical acceleration. The vertical acceleration of the cage lagged the acceleration of the A-frame by up to 1.9 s and its variance was larger by up to a factor 2.2. For moderate displacements of the A-frame (≤2 m), the system is only weakly non-linear because the harmonics (3rd and 5th) of the vertical acceleration of the cage account for less than 2% of the total variance. The system is essentially one-dimensional because only the vertical motion of the cage and the vertical motion of the A-frame were coherent, while horizontal motions of the cage were weak and incoherent with any component of motion of the A-frame. The natural frequency of the system is 0.22 Hz at 1730 m, and we estimate that it is within the waveband for depths between 1450 m and the full operating depth of 5000 m.Large vertical excursions of the A-frame produce momentary slack in the tether near the cage. Retensioning results in snap loads with vertical accelerations of 0.5 gravity. Large rates of change of tension and vertical acceleration first occur at the cage during its downward motion and propagate to the surface with the characteristic speed (3870 m s⁻¹) of tensile waves for the tether. Six echoes are clearly detectable at both ends of the tether, and their pattern is extremely repeatable in different snap loads. Due to misalignment of the tether termination with the centres of mass and buoyancy, the cage pitches by up 14° during a snap. The resulting small radius of curvature poses the greatest stress on the tether.     

Conditional simulation of remotely sensed rainfall data using a non-Gaussian v-transformed copula

Quantification of rainfall and its spatial and temporal variability is extremely important for reliable hydrological and meteorological modeling. While rain gauge measurements do not provide reasonable areal representation of rainfall, remotely sensed precipitation estimates offer much higher spatial resolution. However, uncertainties associated with remotely sensed rainfall estimates are not well quantified. This issue is important considering the fact that uncertainties in input rainfall are the main sources of error in hydrologic processes. Using an ensemble of rainfall estimates that resembles multiple realizations of possible true rainfall, one can assess uncertainties associated with remotely sensed rainfall data. In this paper, ensembles are generated by imposing rainfall error fields over remotely sensed rainfall estimates. A non-Gaussian copula-based model is introduced for simulation of rainfall error fields. The v-transformed copula is employed to describe the dependence structure of rainfall error estimates without the influence of the marginal distribution. Simulations using this model can be performed unconditionally or conditioned on ground reference measurements such that rain gauge data are honored at their locations. The presented model is implemented for simulation of rainfall ensembles across the Little Washita watershed, Oklahoma. The results indicate that the model generates rainfall fields with similar spatio-temporal characteristics and stochastic properties to those of observed rainfall data.     

遥感信息处理不确定性的可视化表达

如何全面、准确地度量和可视化表达遥感信息处理中不确定性的程度和空间分布方式,是遥感信息不确定性研究的关键问题之一.传统的度量方法(例如误差矩阵)是将以训练样本集为基础的度量作为总分类精度的度量,而我们需要估计模型对于"样本外数据"的性能.本文首先利用信息论和粗糙集理论等度量遥感分类影像属性信息的不确定性,提出基于像元、目标和影像的遥感信息不确定性度量指标;然后分别描述了基于不同度量指标的可视化表达方式,并对我国黄河三角洲地区的Landsat TM影像进行了分类信息不确定性度量和可视化表达实验.