An improved simple morphological filter for the terrain classification of airborne LIDAR data

Terrain classification of LIDAR point clouds is a fundamental problem in the production of Digital Elevation Models (DEMs). The Simple Morphological Filter (SMRF) addresses this problem by applying image processing techniques to the data. This implementation uses a linearly increasing window and simple slope thresholding, along with a novel application of image inpainting techniques. When tested against the ISPRS LIDAR reference dataset, SMRF achieved a mean 85.4% Kappa score when using a single parameter set and 90.02% when optimized. SMRF is intended to serve as a stable base from which more advanced progressive filters can be designed. This approach is particularly effective at minimizing Type I error rates, while maintaining acceptable Type II error rates. As a result, the final surface preserves subtle surface variation in the form of tracks and trails that make this approach ideally suited for the production of DEMs used as ground surfaces in immersive virtual environments.     

台湾地区地貌形态的分析研究

数字高程模型(DEM)是地貌解译有力的辅助工具,同时也是对地形地貌分析研究进行量化表达的一个重要手段。在前人研究的方法上通过对研究区台湾地区的SRTM-DEM数据的处理,运用GIS空间分析和统计方法进行地形分析,并在此基础上完成对地形起伏度、坡度、坡向、高程等地形因子相关的拓展分析。根据分析结果对该地区的地貌形态特征进行总结。     

Concurrent Drainage Network Rendering for Automated Pen‐and‐ink Style Landscape Illustration

Pen‐and‐ink style geomorphological illustrations render landscape elements critical to the understanding of surface processes within a viewshed and, at their highest levels of execution, represent works of art, being both practical and beautiful. The execution of a pen‐and‐ink composition, however, requires inordinate amounts of time and skill. This article will introduce an algorithm for rendering creases – linework representing visually significant morphological features – at animation speeds, made possible with recent advances in graphics processing unit (GPU) architectures and rendering APIs. Beginning with a preprocessed high‐resolution drainage network model, creases are rendered from selected stream segments if their weighted criteria (slope, flow accumulation, and surface illumination), attenuated by perspective distance from the viewpoint, exceed a threshold. The algorithm thus provides a methodology for crease representation at continuous levels of detail down to the highest resolution of the preprocessed drainage model over a range of surface orientation and illumination conditions. The article also presents an implementation of the crease algorithm with frame rates exceeding those necessary to support animation, supporting the proposition that parallel processing techniques exposed through modern GPU programming environments provide cartographers with a new and inexpensive toolkit for constructing alternative and attractive real‐time animated landscape visualizations for spatial analysis.     

Urbanization between compactness and dispersion: designing a spatial model for measuring 2D binary settlement landscape configurations

Measuring spatial patterns is a crucial task in spatial sciences. Multiple indicators have been developed to measure patterns in a quantitative manner. However, most comparative studies rely on relative comparisons, limiting their explanatory power to specific case studies. Motivated by advancements in earth observation providing unprecedented resolutions of settlement patterns, this paper suggests a measurement technique for spatial patterns to overcome the limits of relative comparisons. We design a model spanning a feature space based on two metrics – largest patch index and number of patches. The feature space is defined as ‘dispersion index’ and covers the entire spectrum of possible two-dimensional binary (settlement) patterns. The model configuration allows for an unambiguous ranking of each possible pattern with respect to spatial dispersion. As spatial resolutions of input data as well as selected areas of interest influence measurement results, we test dependencies within the model. Beyond, common other spatial metrics are selected for testing whether they allow unambiguous rankings. For scenarios, we apply the model to artificially generated patterns representing all possible configurations as well as to real-world settlement classifications differing in growth dynamics and patterns.     

Integrating optical satellite data and airborne laser scanning in habitat classification for wildlife management

Wildlife habitat selection is determined by a wide range of factors including food availability, shelter, security and landscape heterogeneity all of which are closely related to the more readily mapped landcover types and disturbance regimes. Regional wildlife habitat studies often used moderate resolution multispectral satellite imagery for wall to wall mapping, because it offers a favourable mix of availability, cost and resolution. However, certain habitat characteristics such as canopy structure and topographic factors are not well discriminated with these passive, optical datasets. Airborne laser scanning (ALS) provides highly accurate three dimensional data on canopy structure and the underlying terrain, thereby offers significant enhancements to wildlife habitat mapping. In this paper, we introduce an approach to integrate ALS data and multispectral images to develop a new heuristic wildlife habitat classifier for western Alberta. Our method combines ALS direct measures of canopy height, and cover with optical estimates of species (conifer vs. deciduous) composition into a decision tree classifier for habitat – or landcover types. We believe this new approach is highly versatile and transferable, because class rules can be easily adapted for other species or functional groups. We discuss the implications of increased ALS availability for habitat mapping and wildlife management and provide recommendations for integrating multispectral and ALS data into wildlife management.     

基于分形的图像场表达与图像处理应用

分形图像处理理论是一个尚未形成而又亟待研究的图像处理新理论，本文研究此理论中的几个基本理论问题：３－Ｄ自然景物表面２－Ｄ投影图像的分形投影关系；图像的分形场表达及其适宜性评价，文中以假设检验理论定量地分析了分形场表达的适宜性，并提出以拟合度Ｘ＾２检验法作为确定图像分形场无标度区间的统计量化指标，据此讨论了分形应用于图像处理的基本技术与方法，给出了分形在图像纹理分析，特征提取，目标探测和区域分割等领     

机载激光点云中高压电塔自动识别方法

提出了一种基于格网特征的机载激光点云高压电塔自动识别方法。首先对机载激光点云数据进行滤波去噪处理;然后对点云数据进行规则格网化特征分析获得高压电塔粗识别区域;最后对粗识别区域进行外接邻域网格线性特征悬空点集检测以确定电塔识别结果,并以分层切片法分析获取电塔平面中心坐标。采用大型无人机实际线路巡检获取的机载点云数据对本文算法进行验证,试验结果表明本算法可实现高压电塔的快速自动识别,对无人机电力巡检智能诊断具有一定的促进作用。     

Updating digital elevation models via change detection and fusion of human and remote sensor data in urban environments

OpenStreetMap (OSM) currently represents the most popular project of Volunteered Geographic Information (VGI): geodata are collected by common people and made available for public use. Airborne Laser Scanning (ALS) enables the acquisition of high-resolution digital elevation models that are used for many applications. This study combines the advantages of both ALS and OSM, offering a promising new approach that enhances data quality and allows change detection: the mainly up-to-date 2D data of OSM can be combined with the high-resolution – but rarely updated – elevation information provided by ALS. This case study investigates building objects of OSM and ALS data of the city of Bregenz, Austria. Data quality of OSM is discerned by the comparison of building footprints using different true positive definitions (e.g. overlapping area). High quality of OSM data is revealed, yet also limitations of each method with respect to heterogeneous regions and building outlines are identified. For the first time, an up-to-date Digital Surface Model (DSM) combining 2D OSM and ALS data is achieved. A multitude of applications such as flood simulations and solar potential assessments can directly benefit from this data combination, since their value and reliability strongly depend on an up-to-date DSM.     

基于时间序列分析的露天矿边坡沉降预测模型

结合小波分析在数据处理方面的优势,采用小波包去噪对露天矿边坡沉降数据进行去噪处理,再结合时间序列分析理论建立小波包-时间序列预测模型,从而对露天矿边坡进行变形分析预测。通过实验数据对比分析,结合小波包去噪与时间序列分析理论模型对露天矿边坡沉降数据进行预测,预测精度较高,能够对矿区边坡的沉降进行预测。     

Approaches to Semantic Similarity Measurement for Geo-Spatial Data: A Survey

Semantic similarity is central for the functioning of semantically enabled processing of geospatial data. It is used to measure the degree of potential semantic interoperability between data or different geographic information systems (GIS). Similarity is essential for dealing with vague data queries, vague concepts or natural language and is the basis for semantic information retrieval and integration. The choice of similarity measurement influences strongly the conceptual design and the functionality of a GIS. The goal of this article is to provide a survey presentation on theories of semantic similarity measurement and review how these approaches – originally developed as psychological models to explain human similarity judgment – can be used in geographic information science. According to their knowledge representation and notion of similarity we classify existing similarity measures in geometric, feature, network, alignment and transformational models. The article reviews each of these models and outlines its notion of similarity and metric properties. Afterwards, we evaluate the semantic similarity models with respect to the requirements for semantic similarity measurement between geospatial data. The article concludes by comparing the similarity measures and giving general advice how to choose an appropriate semantic similarity measure. Advantages and disadvantages point to their suitability for different tasks.     

Airborne laser scanning of forest resources: An overview of research in Italy as a commentary case study

This article reviews the recent literature concerning airborne laser scanning for forestry purposes in Italy, and presents the current methodologies used to extract forest characteristics from discrete return ALS (Airborne Laser Scanning) data. Increasing interest in ALS data is currently being shown, especially for remote sensing-based forest inventories in Italy; the driving force for this interest is the possibility of reducing costs and providing more accurate and efficient estimation of forest characteristics. This review covers a period of approximately ten years, from the first application of laser scanning for forestry purposes in 2003 to the present day, and shows that there are numerous ongoing research activities which use these technologies for the assessment of forest attributes (e.g., number of trees, mean tree height, stem volume) and ecological issues (e.g., gap identification, fuel model detection). The basic approaches – such as single tree detection and area-based modeling – have been widely examined and commented in order to explore the trend of methods in these technologies, including their applicability and performance. Finally this paper outlines and comments some of the common problems encountered in operational use of laser scanning in Italy, offering potentially useful guidelines and solutions for other countries with similar conditions, under a rather variable environmental framework comprising Alpine, temperate and Mediterranean forest ecosystems.     

Partial polygon pruning of hydrographic features in automated generalization

This article demonstrates a working method to automatically detect and prune portions of waterbody polygons to support creation of a multi‐scale hydrographic database. Water features are sensitive to scale change, therefore multiple representations are required to maintain visual and geographic logic at smaller scales. Partial pruning of polygonal features – such as long, sinuous reservoir arms, stream channels too narrow at the target scale, and islands that begin to coalesce – entails concurrent management of the length and width of polygonal features as well as integrating pruned polygons with other generalized point and linear hydrographic features to maintain stream network connectivity. The implementation follows data representation standards developed by the US Geological Survey (USGS) for the National Hydrography Dataset (NHD). Portions of polygonal rivers, streams, and canals are automatically characterized for width, length, and connectivity. This article describes an algorithm for automatic detection and subsequent processing, and shows results for a sample of NHD subbasins in different landscape conditions in the US.     

测绘公共服务研究

在社会主义市场经济体制下,测绘公共服务是测绘主管部门的一项基本职能,但由于这个概念提出的时间不长,相关研究尚不多见。本文从测绘公共服务内涵着手,探讨了测绘公共服务的特点、定位等,在深入分析测绘公共服务取得的进展、存在的问题的基础上,提出了进一步完善测绘公共服务的对策措施。     

Effect of field plot location on estimating tropical forest above-ground biomass in Nepal using airborne laser scanning data

The prediction of tropical forest attributes using airborne laser scanning (ALS) is becoming attractive as an alternative to traditional field measurements. Area-based ALS inventories require a set of representative field plots from the study area, which may be difficult to obtain in tropical forests with limited accessibility. This study investigates the effect of sample-plot selection in Nepal, based on two accessibility factors: distance to road and degree of slope. The sparse Bayesian method was employed in the model to estimate above-ground biomass (AGB) with an independent validation dataset for model validation. Study findings showed that the sample plot distance and slope had a considerable effect on the accuracy of the AGB estimation, because the forest structure varied according to the level of accessibility. Thus, the field sample plots that are used in model construction should cover the full range of sample plot distances and slopes occurring within the area.     

智能遥感深度学习框架与模型设计EI北大核心CSCD

近年来,随着遥感技术的快速发展,遥感对地观测数据获取量与日俱增。在对海量遥感数据的特征提取与表征上,基于深度学习的智能遥感影像解译技术展现出了显著优势。然而,遥感影像智能处理框架和信息服务能力还相对滞后,开源的深度学习框架与模型尚不能满足遥感智能处理的需求。在分析现有深度学习框架和模型的基础上,针对遥感影像幅面大、尺度变化大、数据通道多等问题,本文设计了嵌入遥感特性的专用深度学习框架,并重点讨论了其构建方法,以及地物分类任务的初步试验结果等。本文提出的智能遥感解译框架架构将为构建具备多维时空谱遥感特性的深度学习框架与模型提供有力支撑。     

Integrating Arc Hydro Features with a Schematic Network

A framework for integrating GIS features with processing engines to simulate hydrologic behavior is presented. The framework is designed for compatibility with the ArcGIS ModelBuilder environment, and utilizes the data structure provided by the SchemaLink and SchemaNode feature classes from the ArcGIS Hydro data model. SchemaLink and SchemaNode form the links and nodes, respectively, in a schematic network representing the connectivity between hydrologic features pertinent to the movement of surface water in the landscape. A specific processing engine is associated with a given schematic feature, depending on the type of feature the schematic feature represents. Processing engines allow features to behave as individual hydrologic processors in the landscape. The framework allows two types of processes for each feature, a Receive process and a Pass process. Schematic network features operate with four types of values: received values, incremental values, total values, and passed values. The framework assumes that the schematic network is dendritic, and that no backwater effects occur between schematic features. A case study is presented for simulating bacterial loading in Galveston Bay in Texas from point and nonpoint sources. A second case study is presented for simulating rainfall‐runoff response and channel routing for the Llano River in Texas.     

Automating Landscape Illustration with Pen and Ink Style Rendering

This paper introduces object space procedures for extracting silhouettes, slope lines, and drainage features from digital elevation models (DEMs) to direct the rendering of landform features in the style of pen-and-ink landscape illustrations. Unlike image space procedures which generally extract feature information through 2-dimensional (2D) rendered image processing techniques, object space procedures operate directly on 3-dimensional (3D) surface models. The ultimate goal of this work is to produce fully automated tools that can imitate the styles characterized by the traditional pen-and-ink techniques of Lobeck, Imhof, Raisz, and other scientific illustrators to bring the beauty of their effective and economical visual techniques to automated cartographic environments. Through the implementation of a Java 3D application programming interface (API) prototype, a testing platform was established for the application of stylistic elements to linework representing surface form lines. This paper explores the aesthetic effects of silhouette lines, creases, and slope lines on the rendering of terrain features. It also introduces the use of adaptively resampled triangulated irregular networks (TINs) as a basis for perspective rendering applications.     

A DEM generalization by minor valley branch detection and grid filling

As an important method of terrain representation, a DEM usually needs to be generalized at multiple resolutions in order to adapt to different applications. The preservation of main landscape features is an important constraint in DEM generalization. The traditional generalization method based on signal processing by resampling or low-pass filtering is just a data compression operation rather than the abstraction of real information. This study develops a structured analysis method to generalize DEM data through the identification of minor valleys and filling the corresponding depression positions. The generalization process has two steps: geographic decision and geometric operation. According to their hydrological significance, the unimportant valley branches are detected and their corresponding coverage is filled by raising the terrain to make the terrain surface smoother. In contrast to the conventional algorithms based on image processing, this method is able to retain the main geographical characteristics more effectively in terrain representation.     

Remote Targets Recognition Based on Adaptive Weighting Feature Dictionaries and Joint Sparse Representations

With the improvement in resolution, more and more useful information is contained in the space of remote sensing images, which makes the processing of remote sensing data become more complex, and it is easy to cause the curse of dimensionality and the poor recognition effect. In this paper, a remote target recognition approach named AJRC is proposed, which uses joint feature dictionary for sparse representation based on different feature information for adaptive weighting. Firstly, the features of the images are extracted to calculate the contribution weight of each eigenvalue in sparse representation, and each eigenvalue contribution weight is calculated in sparse representation. Through the adaptive method, the contribution ability of each feature value in sparse representation is strengthened, and new atoms are formed to construct feature dictionary, which makes the dictionary more discriminative. Then, the common features of each category image and the private features of a single image are extracted from the feature vector, and a joint dictionary is formed to represent the test image sparse and recognize the output of the target. Aiming at the problem that the target visual contrast difference, the low resolution and the rotation of the target with different angles, the experiment is carried out by different feature extraction methods. At the same time, we use the PCA method to reduce the feature dictionary in order to avoid dimensionality. Experiments show that compared with the existing SRC method and JSM method, this method has better recognition rate.