Scale- and orientation-invariant scene similarity metrics for image queries

In this paper we extend our previous work on shape-based queries to support queries on configurations of image objects. Here we consider spatial reasoning, especially directional and metric object relationships. Existing models for spatial reasoning tend to rely on pre-identified cardinal directions and minimal scale variations, assumptions that cannot be considered as given in our image applications, where orientations and scale may vary substantially, and are often unknown. Accordingly, we have developed the method of varying baselines to identify similarities in direction and distance relations. Our method allows us to evaluate directional similarities without a priori knowledge of cardinal directions, and to compare distance relations even when query scene and database content differ in scale by unknown amounts. We use our method to evaluate similarity between a user-defined query scene and object configurations. Here we present this new method, and discuss its role within a broader image retrieval framework.     

Editorial

Abstract

With the increase in volume of spatial data now available, more effective ways must be found of storing and processing these data. This paper presents a compacted version of the linear quadtree and a spatially-referenced index method that can significantly reduce the storage requirements of a set of images and the time taken to process spatial queries. The index acts as a high-level summary of a regular-sized portion of the underlying image and so can be used to avoid examining areas of the image where none of the required features is present. Some example results are given. A method for the optimization of spatial searches is presented which takes into account the area and distribution of features within an image. Finally, a method for directly associating the edges of features with the individual nodes of a quadtree is reported. This is important since the edges of objects are no longer explicitly present in linear quadtrees and so must be recalculated when they are required for part of a query. Recalculation of object edges or boundaries is expensive; it is best, therefore, to perform the operation once only, and then save the results.     

Why not SQL!

Abstract

The application of traditional database query languages, primarily the Structured Query Language SQL, for geographical information systems (GIS) and other non–standard database applications has been tried unsuccessfully; therefore, several extensions to the relational database query language SQL have been proposed to serve as a spatial query language. It is argued that the SQL framework is inappropriate for an interactive query language for a GIS and an extended SQL is at best a short term solution. Any spatial SQL dialect has a number of serious deficiencies, particularly the patches to incorporate the necessary spatial concepts into SQL.     

地理空间意像模式的Voronoi模型

提出用Voronoi空间模型来表达意像模式，Voronoi模型无岐义空间邻近关系，构建能封装对象间空间关系的拓扑网络，使用该模型将各种空间介词映射为不同的拓扑结构，GIS采用该模型，可按自然语言中空间介词描述的定性空间关系查询检索模糊地理信息。     

基于OGC WPS的遥感图像分布式检索系统研究

结合OGC WPS开放标准规范和分布式检索技术,提出基于OGC WPS构建遥感图像分布式检索系统的方法。采用Z39.50通用检索表达式的扩展模式,避免了遥感图像编目数据库与检索系统的紧密耦合;采用HTTP/XML定义检索网关和资源节点之间的WPS接口,实现了编目资源节点到网关的动态接入与移出机制。最后运用开放源代码GIS技术开发了一个原型演示系统,试验证明上述方法切实可行。     

A Spatial Dependence Approach to Retrieving Industrial Complexes from Digital Images*

This article reports preliminary results of using a spatial dependence approach to retrieving industrial building complexes from digital aerial photographs. Because image retrieval was originally developed outside geography, this paper first discusses the principle of image retrieval in the context of geographic studies, the basic types of geographic features for retrieval, and the spatially dependent nature of geographic features. Based on these discussions, the spatial dependence approach is presented for the intended retrieval. Semivariogram is used to represent the spatial dependence of geographic features and for the subsequent retrieval of these features. Results show the effectiveness of this approach and warrant further investigations.     

A Spatial Dependence Approach to Retrieving Industrial Complexes from Digital Images

This article reports preliminary results of using a spatial dependence approach to retrieving industrial building complexes from digital aerial photographs. Because image retrieval was originally developed outside geography, this paper first discusses the principle of image retrieval in the context of geographic studies, the basic types of geographic features for retrieval, and the spatially dependent nature of geographic features. Based on these discussions, the spatial dependence approach is presented for the intended retrieval. Semivariogram is used to represent the spatial dependence of geographic features and for the subsequent retrieval of these features. Results show the effectiveness of this approach and warrant further investigations.     

基于多重分割关联子的高分辨率遥感场景分类

高分辨率遥感影像提供了丰富的外观信息和空间结构信息,广泛应用于土地利用分类当中,源于文章领域的视觉词袋（Bag-of-Visual-Words,BoVW）模型现已成功应用于图像分类领域。传统的BoVW模型忽略了特征之间的空间布局信息和像素一致性信息,提出多重分割关联子特征,融合图像的外观信息、空间布局信息和像素一致性信息,实验表明该方法能够获取优于许多经典的遥感图像特征的性能。     

基于语义和边缘特征融合的高分辨率遥感影像水体提取方法

利用卷积神经网络从遥感影像中提取水体时,水体对象边缘像素的特征与内部像素的特征之间往往存在较大差异,导致提取结果中边界模糊、内部像素与边缘像素的提取精度差异较大,影响了整体精度的提高。针对如何从高分辨率遥感影像中进行水体高精度、自动化提取的问题,文章首先以高分辨率遥感图像为基础,利用边缘提取算法生成边缘图像,然后以高分辨率遥感图像和边缘图像作为输入,建立了语义特征和边缘特征融合的高分辨率遥感图像水体提取模型（Semantic Feature and Edge Feature Fusion Network, SEF-Net）,用于从高分辨率遥感图像中提取水体对象。实验结果表明,SEF-Net模型在3个数据集中的召回率（91.97%、92.07%、93.97%）,精确率（91.12%、98.37%、97.88%）,准确率（89.56%、95.07%、94.06%）和F1分数（91.54%、95.12%、95.88%）均优于对比模型,说明SEF-Net模型从高分辨率遥感图像中提取水体时,具有更高的精度和泛化能力。     

基于WebGIS的TB级南极遥感影像发布系统关键技术研究

我国已经完成南极地区多种类型高分辨率遥感影像的制图,其产品的数据量达到TB级,对这些影像进行网络共享有助于帮助人们更好地认识南极,也能提升我国在南极事务中的影响力.本文介绍了基于数据库以及WebGIS平台的TB级南极洲遥感影像图网络发布系统的设计与实现方法.以ArcGIS Server作为WebGIS平台,对各类遥感影...     

具有更佳分辨率小波分解的遥感影像纹理分类*

首先提出了具有更佳分辨率的小波分解,然后研究了基于该小波分解特征的影像纹理分类,并对25类地貌遥感影像在两种不同分解方式、两种不同滤波器长度及三种不同分辨率下进行了分类试验,取得了较高的分类正确率。     

Image‐based quality assessment of road databasesX

In this paper an approach to the automatic quality assessment of existing geo‐spatial data is presented. The necessary reference information is derived automatically from up‐to‐date digital remotely sensed images using image analysis methods. The focus is on the quality assessment of roads as these are among the most frequently changing objects in the landscape. In contrast to existing approaches for quality control of road data, the data to be assessed and the objects extracted from the images are modelled and processed together. A geometric‐topologic relationship model for the roads and their surroundings is defined. Context objects such as rows of trees support the quality assessment of road vector data as they may explain gaps in road extraction. The extraction and explicit incorporation of these objects in the assessment of a given road database give stronger support for or against its correctness.

During the assessment existing relations between road objects from the database and extracted objects are compared to the modelled relations. The certainty measures of the objects are integrated into this comparison. Normally, more than one extracted object gives evidence for a road database object; therefore, a reasoning algorithm which combines evidence given by the extracted objects is used. If the majority of the total evidence argues for the database object and if a certain amount of this database object is covered by extracted objects, the database object is assumed to be correct, i.e. it is accepted, otherwise it is rejected. The procedure is embedded into a two‐stage graph‐based approach which exploits the connectivity of roads and results in a reduction of false alarms. The algorithms may be incorporated into a semi‐automatic environment, where a human operator only checks those objects that have been rejected.

The experimental results confirm the importance of the employed advanced statistical modelling. The overall approach can reliably assess the roads from the given database, using road and context objects which have been automatically extracted from remotely sensed imagery. Sensitivity analysis shows that in most cases the chosen two‐stage graph‐approach reduces the number of false decisions. Approximately 66% of the road objects have been accepted by the developed approach in an extended test area, 1% has been accepted though incorrect. Those false decisions are mainly related to the lack of modelling road junction areas.     

黄河下游堤内滩地土地利用的GIS分析

应用航空彩红外土地利用解译图,以ARC INFO为支撑软件,建立了黄河下游河道滩地(花园口至孙口河段)土地利用现状数据库。通过空间拓朴叠加分析,得到不同类型滩地的土地利用状况。并以数字高程模型为基础,本文分析土地利用与河道滩地的空间分布规律。     

基于空间数据库技术的地籍管理系统研究

随着GIS应用的深入发展,以文件管理空间数据的方式已不能满足当前GIS应用的需要,而空间数据库技术正是为解决GIS应用的企业化和社会化问题而发展起来的新技术,空间数据库技术及其应用研究是当前GIS技术研究的热点。本文分析了空间数据对象、空间数据库、空间数据库技术及其实现,研究了基于空间数据库技术的地籍管理系统在空间数据共享、海量数据管理、大范围查询检索、分布式空间数据处理与维护、地籍制图、数据安全控制等方面的应用实现,通过具体应用系统建设,提出了基于空间数据库技术的地籍管理信息系统的解决方案。     

Automated terrain feature identification from remote sensing imagery: a deep learning approach

ABSTRACT

Terrain feature detection is a fundamental task in terrain analysis and landscape scene interpretation. Discovering where a specific feature (i.e. sand dune, crater, etc.) is located and how it evolves over time is essential for understanding landform processes and their impacts on the environment, ecosystem, and human population. Traditional induction-based approaches are challenged by their inefficiency for generalizing diverse and complex terrain features as well as their performance for scalable processing of the massive geospatial data available. This paper presents a new deep learning (DL) approach to support automatic detection of terrain features from remotely sensed images. The novelty of this work lies in: (1) a terrain feature database containing 12,000 remotely sensed images (1,000 original images and 11,000 derived images from data augmentation) that supports data-driven model training and new discovery; (2) a DL-based object detection network empowered by ensemble learning and deep and deeper convolutional neural networks to achieve high-accuracy object detection; and (3) fine-tuning the model’s characteristics and behaviors to identify the best combination of hyperparameters and other network factors. The introduction of DL into geospatial applications is expected to contribute significantly to intelligent terrain analysis, landscape scene interpretation, and the maturation of spatial data science.     

基于面向对象的城市地物信息提取方法研究

20世纪90年代以来,高空间分辨率遥感影像数据的处理已成为遥感领域中的热点与难点。利用具有人的思维特点的面向对象的信息提取技术,对高分辨率遥感影像中的城市用地进行分类,分析和利用高分辨率影像的空间信息、结构信息与光谱特征等,总结了面向对象解译方法的5个步骤,即影像分割、分类方法的选择、地物种类分类,知识库构建、计算机自动分类。分类结果表明：（1）克服了“椒盐现象”;（2）信息提取的总体精度为92.19％,而且各类地物信息的提取精度均有所提高,特别是利用前期分类的拓扑关系有效提取了城市水体与建筑物阴影。     

An OLS regression model for context-aware tile prefetching in a web map cache

The increasing popularity of web map services has motivated the development of more scalable services in the spatial data infrastructures. Tiled map services have emerged as a scalable alternative to traditional map services. Instead of rendering map images on the fly, a collection of pre-generated image tiles can be served very fast from a server-side cache. However, during the start-up of the service, the cache is initially empty and users experience a poor quality of service. Tile prefetching attempts to improve hit rates by proactively fetching map images without waiting for client requests.

While most popular prefetching policies in traditional web caching consider only the previous access history to make predictions, significant improvements could be achieved in web mapping by taking into account the background geographic information.

This work proposes a regressive model to predict which areas are likely to be requested in the future based on spatial cross-correlation between an unconstrained catalog of geographic features and a record of past cache requests. Tiles that are anticipated to be most frequently requested can be pre-generated and cached for faster retrieval. Trace-driven simulations with several million cache requests from two different nation-wide public web map services in Spain demonstrate that accurate predictions and performance gains can be obtained with the proposed model.