Automated evaluation of building alignments in generalized maps

Evaluation is a key step to examine the quality of generalized maps with respect to map requirements. Map generalization facilitates the recognition of pattern generating processes by preserving and highlighting the patterns at smaller scales. This article focuses specifically on the evaluation of building patterns in topographic maps that are generalized from large to mid scales. Currently, there is a lack of knowledge and functionality on automatically evaluating how these patterns are generalized. The issues of the evaluation range from missing formal map requirements on building alignments to missing automated evaluation techniques. This article firstly analyses the requirements (constraints) related to the generalization of building alignments. Then, it focuses on three more specific constraints, i.e. on existence, orientation of alignments and spatial distribution of composing buildings. Later, a three-step approach is proposed to (1) recognize and (2) match alignments from source and generalized datasets and (3) evaluate building alignments in generalized datasets. Besides, many-to-many and partial matching between initial and target alignments is a side effect of generalization, which reduces the reliability of the evaluation results. This article introduces a confidence indicator to document the reliability and to inform intended users (e.g. cartographers) and/or systems about the reliability of evaluation decisions. The effectiveness of our approach is demonstrated by evaluating the alignments in both interactively (manually) generalized maps and automated generalized maps. Finally, we discuss how our approach can be used to control automated generalization and identify further improvements.     

A probabilistic relaxation approach for matching road networks

Geospatial data matching is an important prerequisite for data integration, change detection and data updating. At present, crowdsourcing geospatial data are attracting considerable attention with its significant potential for timely and cost-effective updating of geospatial data and Geographical Information Science (GIS) applications. To integrate the available and up-to-date information of multi-source geospatial data, this article proposes a heuristic probabilistic relaxation road network matching method. The proposed method starts with an initial probabilistic matrix according to the dissimilarities in the shapes and then integrates the relative compatibility coefficient of neighbouring candidate pairs to iteratively update the initial probabilistic matrix until the probabilistic matrix is globally consistent. Finally, the initial 1:1 matching pairs are selected on the basis of probabilities that are calculated and refined on the basis of the structural similarity of the selected matching pairs. A process of matching is then implemented to find M:N matching pairs. Matching between OpenStreetMap network data and professional road network data shows that our method is independent of matching direction, successfully matches 1:0 (Null), 1:1 and M:N pairs, and achieves a robust matching precision of above 95%.     

岷江流域无人机遥感影像拼接及灾害信息提取

针对无人机遥感影像的特点,提出了一种基于特征点和重叠度的快速自动拼接算法.该算法实现了用于计算参考图像和待配准图像之间重叠度的相位相关法,并提出只在重叠区域中进行特征提取和特征匹配的方法.利用岷江流域无人机遥感图像进行了实验,结果表明,该算法使图像拼接有效地防止了图像非重叠区域中信息对算法的干扰,提高了拼接算法的精度和速度.并利用拼接的图像对茂县至都江堰的岷江主干河流进行了灾害信息提取和统计分析,为建立无人机高分辨率低空遥感数据的快速处理和应急灾害信息提取具有重要的指导意义.     

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Recent advances in earthquake monitoring II: Emergence of next-generation intelligent systems

Seismic data processing techniques, together with seismic instrumentation, determine our earthquake monitoring capability and the quality of resulting earthquake catalogs. This paper is intended to review the improvement of earthquake monitoring capability from the perspective of data processing. Over the past two decades, seismologists have made considerable advancements in seismic data processing, partly thanks to the significant development of computational power, signal processing, and machine learning techniques. In particular, wide application of template matching and increasing use of deep learning significantly enhance our capability to extract signals of small earthquakes from noisy data. Relative location techniques provide a critical tool to elucidate fault geometries and seismicity migration patterns at unprecedented resolution. These techniques are becoming standard, leading to emerging intelligent software systems for next-generation earthquake monitoring. Prospective improvements in future research must consider the urgent needs in highly generalizable detection algorithms (for both permanent and temporary deployments) and in emergency real-time monitoring of ongoing sequences (e.g., aftershock and induced seismicity sequences). We believe that the maturing of intelligent and high-resolution processing systems could transform traditional earthquake monitoring workflows and eventually liberate seismologists from laborious catalog construction tasks.     

Change detection based on DSM and image features in urban areas

On the basis of atereo image analysis,the change detection of man-made objects in urban areas is in-troduced. Information of the height of man-made objects can be applied to reinforce their change detection. By comparison between the new and old DSMs, the changed regions are extracted. However, our aim is to detect changes of man-made objects in urban area and further in the potental areas by the means of line-feature matching and gradient direction histogram. The experiments based on the aerial images from Japan have proven that the algorithm is correct and efficient.     

基于SUSAN算法的航空影像的自动配准

图像配准技术是图像融合、图像镶嵌以及影像三维重建的基础。提出了一种基于SU-SAN(Smallest Univalue Segment Assimilating Nucleus)算子的图像配准方法。该方法利用SU-SAN算子提取两幅图像的角点,通过粗匹配和细匹配两个步骤得到匹配角点对。然后再根据这些角点对对图像进行配准。试验表明此方法能有效地实现航空影像自动配准。     

威特DI2002测距仪性能及其与ME－3000反射镜匹配研究

本文通过对威特ＤＩ２００２测距仪性能的测试。发现影响测距精度的主要因素是ＧＲＴ１０－ＧＰＨ１Ａ的反射镜系统。威特ＤＩ２００２测距仪匹配ＭＥ－３０００反射镜后，需加两项改正，但观测精度显著提高，实测表明达到与ＭＥ－３０００测距仪相同的精度。     

基于角点特征的近景影像匹配算法

利用了改进的Harris特征点提取算子从原始立体像对提取一系列特征点,采用了自适应三角形约束下的影像匹配方法进行近景影像匹配,试验结果表明其具有良好的可靠性。     

基于小波分量特征值匹配的高光谱影像分类

提出了一种基于小波分量特征值的高光谱影像分类算法。针对每个像素构建一个能反映该分量特征的函数，得到其特征值。再利用这些特征值与参考光谱的特征值进行匹配，从而对整幅影像实现分类。实验证明，该方法比传统的光谱角制图法和交叉相关系数法的分类精度有较大提高。