GIS支持下的遥感专题地图制作

本文以土地利用遥感影像图为例，介绍了在工作站版ＥＲＤＡＳ系统中，将经过专题信息提取得到的遥感专题影像图，通过制图综合、栅格矢量转换、专题符号设计、面域填充、图面整饰等过程制作遥感专题地图的步骤和方法。与原影像图相比，此类地图易读，表现能力强，使遥感专题地图的表现方法更加实用化、规范化，符合了不同专业的制图要求     

基于ArcGIS与Illustrator的长城专题影像图制作

长城专题影像图作为长城测量项目重要的成果之一,是以航空遥感影像为基础,负载大量现势性强的基础空间数据和长城专题信息数据,从而可以满足长城修复、保护等各项工作。通过分析当前专题图制作的两种主要技术,提出基于地理信息系统软件（ArcGIS）和专业绘图软件（Illustrator）联合实现专题图制作的方法,以设计和制作长城专题影像图为例,详细介绍了该模式下专题影像图的整体设计、数据组织及处理、图廓整饰等。通过应用说明：这种混合制图方式不仅可提高绘图精度、丰富地图内容表示等方面具有明显优势,而且能提高绘图效率、降低工作成本,在专题地图制作方面具有广阔的应用前景。     

在Inspiration中实现概念地图

在本刊2003年第9期的"焦点"和"对话"栏目中,我们刊登了专题文章,较系统地介绍了概念地图的概念和在教学中的具体应用,在读者中反响良好.本文介绍了在可视化的思维工具Inspiration中制作概念地图的基本方法. Inspiration是一个可视化的思维工具,它是一个以制作概念地图为主要功能的教学软件.     

基于Google影像的城市道路网提取及其应用

从遥感影像中提取道路信息制作专题图方法具有时效性强、周期短、操作快捷等特点。首先介绍了道路提取、处理以及专题图制作的方法与流程。其次以Google高分辨率影像为数据源,利用影像中道路的光谱和几何特征信息,结合计算机分类与形态学处理方法对实验区城市道路网络进行提取、处理和专题图的制作。实验结果表明:该方法能够清晰地提取出道路框架,满足一定的制图需求,而且对车辆、人群、阴影产生的道路内部空洞和边缘侵蚀具有很好的处理效果,具有较强的经济性和实用性。     

基于Internet的网络地图交互制图实现研究

网络地图是指在万维网(WWW)上浏览、编辑、制作的地图,它作为一种空间信息的可视化与传播形式不仅包括在Web上浏览地图,还包括在Web上编辑制作地图.目前网络地理信息系统中仅仅做到对网络地图的发布已不能够满足广大WebGIS用户的工作需求,因此在网络地理信息系统中还应提供网络地图在浏览器端的交互制图功能.针对这一问题提出了基于Internet的网络地图交互制图的实现方法,并将其应用到了重庆市渝北区土地利用规划管理信息系统中,成功地实现了在浏览器端对地图进行交互制图操作.     

利用ARCGIS实现多级比例尺地图显示研究

大量GIS应用的地图显示功能具有多尺度特性。GIS中空间数据的多比例尺表达和处理的概念与制图综合既有联系,又有区别。本文旨在研究满足GIS应用目的空间数据多比例尺显示和制作的问题,基于大量文献对空间比例尺、人工制图综合、数字制图综合、地理数据库、地图数据库以及概念框架的相互关系进行了研究和探讨,使用ARCGIS实现空间数据的多比例尺表达与处理。     

基于层级实时记忆的地图创建方法?

提出基于层级实现记忆( HTM)网络的地图创建方法。该方法利用层级实时记忆将制图问题等效为场景识别问题,环境地图由一系列HTM模型输出的场景构成。首先从获取图像中提取位置不变鲁棒特征( PIRF)。并利用PIRF构建视觉词汇表,根据词汇表将图像的PIRF描述符映射为视觉单词频率矢量。多个视觉单词频率矢量构成的序列输入HTM网络,用于实现环境地图的学习与创建及环路场景的推断识别。采用两组实验数据验证文中方法,结果表明基于HTM的制图策略能成功建立环境地图,并能高效处理环路检测问题。     

基于深度强化学习的室内视觉局部路径规划

传统的机器人局部路径规划方法多为已有先验地图的情况设计,导致其在与视觉(simultaneous localization and mapping, SLAM)结合的导航中效果不佳。为此传统的机器人局部路径规划方法多为已有先验地图的情况设计,导致其在与视觉SLAM结合的导航中效果不佳。为此,本文提出一种基于深度强化学习的视觉局部路径规划策略。首先,基于视觉同时定位与建图(SLAM)技术建立周围环境的栅格地图,并使用A*算法规划全局路径;其次,综合考虑避障、机器人行走效率、位姿跟踪等问题,构建基于深度强化学习的局部路径规划策略,设计以前进、左转、右转为基本元素的离散动作空间,以及基于彩色图、深度图、特征点图等视觉观测的状态空间,利用近端策略优化(proximal policy optimization, PPO)算法学习和探索最佳状态动作映射网络。Habitat仿真平台运行结果表明,所提出的局部路径规划策略能够在实时创建的地图上规划出一条最优或次优路径。相比于传统的局部路径规划算法,平均成功率提高了53.9%,位姿跟踪丢失率减小了66.5%,碰撞率减小了30.1%。     

遥感影像土地分类结果的矢量化研究

阐述了将遥感影像的土地利用分类结果由栅格数据格式转换为矢量数据格式的一种较为通用的方法,即经过影像分类提取相关的土地利用专题信息,如地表水体分布信息、建设用地(包括建筑物和道路)分布信息等,再通过对分类结果数据的处理和转换,获取GIS矢量数据,以满足进一步的专题信息制图和数据库建设等方面的应用需要.     

遥感制图及其研究特点

遥感科学促进了现代地图学的进步,导致遥感制图的崛起,使之形成现代地图学中的一个新分支——遥感制图学。可以认为它是新技术学科与相邻科学间互相渗透的结果。遥感制图学发展的特征,基本上可归纳为: (1) 制图信息源日趋多样,数据的采集与分析技术不断革新,促进了遥感制图的自动化; (2) 多种信息复合的研究和标准影像地图系列化的研究是遥感专题制图的发展趋势; (3) 多时相图像的综合分析与环境动态制图是目前遥感信息制图的一个重要途径; (4) 信息系列转换及制图工艺的研究是成图精度和效益的基本保证; (5) 信息分析与制图趋向标准化、规范化,国际制图趋向系统化。     

The use of SPOT-simulated imagery in hydrological mapping

Abstract

The fine spatial resolution offered by the SPOT system promises great improvements in the mapping of surface water and drainage networks. Simulated SPOT imagery of sites covering a variety of landscape units in Wales were examined. Identification of streams on the basis of spatial characteristics alone was found to be inefficient, and the use of thematic information was found beneficial. Strategies for interactive image enhancement are discussed, and the results of a visual interpretation of standard photoproducts are presented. The results of the interpretation of simulated SPOT data are compared with results of an interpretation of LANDSAT MSS data.     

Learning features from georeferenced seafloor imagery with location guided autoencoders

Although modern machine learning has the potential to greatly speed up the interpretation of imagery, the varied nature of the seabed and limited availability of expert annotations form barriers to its widespread use in seafloor mapping applications. This motivates research into unsupervised methods that function without large databases of human annotations. This paper develops an unsupervised feature learning method for georeferenced seafloor visual imagery that considers patterns both within the footprint of a single image frame and broader scale spatial characteristics. Features within images are learnt using an autoencoder developed based on the AlexNet deep convolutional neural network. Features larger than each image frame are learnt using a novel loss function that regularises autoencoder training using the Kullback–Leibler divergence function to loosely assume that images captured within a close distance of each other look more similar than those that are far away. The method is used to semantically interpret images taken by an autonomous underwater vehicle at the Southern Hydrates Ridge, an active gas hydrate field and site of a seafloor cabled observatory at a depth of 780 m. The method's performance when applied to clustering and content‐based image retrieval is assessed against a ground truth consisting of more than 18,000 human annotations. The study shows that the location based loss function increases the rate of information retrieval by a factor of two for seafloor mapping applications. The effects of physics‐based colour correction and image rescaling are also investigated, showing that the improved consistency of spatial information achieved by rescaling is beneficial for recognising artificial objects such as cables and infrastructures, but is less effective for natural objects that have greater dimensional variability.     

Mapping coral reefs at reef to reef-system scales, 10s–1000s km2, using object-based image analysis

Coral reef maps at various spatial scales and extents are needed for mapping, monitoring, modelling, and management of these environments. High spatial resolution satellite imagery, pixel <10 m, integrated with field survey data and processed with various mapping approaches, can provide these maps. These approaches have been accurately applied to single reefs (10–100 km²), covering one high spatial resolution scene from which a single thematic layer (e.g. benthic community) is mapped. This article demonstrates how a hierarchical mapping approach can be applied to coral reefs from individual reef to reef-system scales (10–1000 km²) using object-based image classification of high spatial resolution images guided by ecological and geomorphological principles. The approach is demonstrated for three individual reefs (10–35 km²) in Australia, Fiji, and Palau; and for three complex reef systems (300–600 km²) one in the Solomon Islands and two in Fiji. Archived high spatial resolution images were pre-processed and mosaics were created for the reef systems. Georeferenced benthic photo transect surveys were used to acquire cover information. Field and image data were integrated using an object-based image analysis approach that resulted in a hierarchically structured classification. Objects were assigned class labels based on the dominant benthic cover type, or location-relevant ecological and geomorphological principles, or a combination thereof. This generated a hierarchical sequence of reef maps with an increasing complexity in benthic thematic information that included: ‘reef’, ‘reef type’, ‘geomorphic zone’, and ‘benthic community’. The overall accuracy of the ‘geomorphic zone’ classification for each of the six study sites was 76–82% using 6–10 mapping categories. For ‘benthic community’ classification, the overall accuracy was 52–75% with individual reefs having 14–17 categories and reef systems 20–30 categories. We show that an object-based classification of high spatial resolution imagery, guided by field data and ecological and geomorphological principles, can produce consistent, accurate benthic maps at four hierarchical spatial scales for coral reefs of various sizes and complexities.     

Object-based image analysis for operational fine-scale regional mapping of land cover within river corridors from multispectral imagery and thematic data

Accurate mapping of land-cover diversity within riparian areas at a regional scale is a major challenge for better understanding the influence of riparian landscapes and related natural and anthropogenic pressures on river ecological status. As the structure (composition and spatial organization) of riparian area land cover (RALC) is generally not accessible using moderate-scale satellite imagery, finer spatial resolution imagery and specific mapping techniques are needed. For this purpose, we developed a classification procedure based on a specific multiscale object-based image analysis (OBIA) scheme dedicated to producing fine-scale and reliable RALC maps in different geographical contexts (relief, climate and geology). This OBIA scheme combines information from very high spatial resolution multispectral imagery (satellite or airborne) and available spatial thematic data using fuzzy expert knowledge classification rules. It was tested over the Hérault River watershed (southern France), which presents contrasting landscapes and a total stream length of 1150 km, using the combination of SPOT (Système Probatoire d'Observation de la Terre) 5 XS imagery (10 m pixels), aerial photography (0.5 m pixels) and several national spatial thematic data. A RALC map was produced (22 classes) with an overall accuracy of 89% and a kappa index of 83%, according to a targeted land-cover pressures typology (six categories of pressures). The results of this experimentation demonstrate that the application of OBIA to multisource spatial data provides an efficient approach for the mapping and monitoring of RALC that can be implemented operationally at a regional or national scale. We further analysed the influence of map resolution on the quantification of riparian spatial indicators to highlight the importance of such data for studying the influence of landscapes on river ecological status at the riparian scale.     

A novel adaptive fast IHS transform fusion method driven by regional spectral characteristics for Gaofen-2 imagery

ABSTRACT

With the increasing diversity of applications based on the Gaofen-2 satellite imagery, broadly applicable methods to generate high quality fused images is a significant problem to investigate. To obtain an image with high spatial and spectral resolutions from given panchromatic (Pan) images and multispectral (MS) images, most existing fusion algorithms adopt a unified strategy for the whole image. However, regions have distinct characteristics that impact the spatial and spectral resolution processing, on account of their varying regional features. In this article, to satisfy the diverse needs of different regions, a novel fast IHS (Intensity-Hue-Saturation) transform fusion method driven by regional spectral characteristics is proposed to fuse Gaofen-2 imagery. First, by the fast IHS transform framework, the original intensity component is obtained from the upsampled MS imagery. Then, numerous independent regions of upsampled MS imagery are generated by a novel superpixel merging strategy, and the spectral characteristics of these regions are utilized for generating a fusion factor. Next, to acquire a new fused intensity component, the fusion factor is applied to guide the injection of details in the fusion procedure. This fusion factor adapts the method to meet the spatial and spectral resolution needs for each region. Finally, the difference between the new fused intensity component and the original one is regarded as the detail that needs to be injected; these are added equally to the different bands of the upsampled MS imagery to yield the final fused multispectral image. In comparison with other classical algorithms, the visual and statistical analysis reveal that our proposed method can provide better results in improving spatial detail and preserving spectral information.     

Sub-pixel mapping and sub-pixel sharpening using neural network predicted wavelet coefficients

Sub-pixel mapping and sub-pixel sharpening are techniques for increasing the spatial resolution of sub-pixel image classifications. The proposed method makes use of wavelets and artificial neural networks. Wavelet multiresolution analysis facilitates the link between different resolution levels. In this work a higher resolution image is constructed after estimation of the detail wavelet coefficients with neural networks. Detail wavelet coefficients are used to synthesize the high-resolution approximation. The applied technique allows for both sub-pixel sharpening and sub-pixel mapping. An algorithm was developed on artificial imagery and tested on artificial as well as real synthetic imagery. The proposed method resulted in images with higher spatial resolution showing more spatial detail than the source imagery. Evaluation of the algorithm was performed both visually and quantitatively using established classification accuracy indices.     

The effects of user factors and symbol referents on public symbol design using the stereotype production method

This study investigated the influence of user factors and symbol referents on public symbol design among older people, using the stereotype production method for collecting user ideas during the symbol design process. Thirty-one older adults were asked to draw images based on 28 public symbol referents and to indicate their familiarity with and ease with which they visualised each referent. Differences were found between the pictorial solutions generated by males and females. However, symbol design was not influenced by participants' education level, vividness of visual imagery, object imagery preference or spatial imagery preference. Both familiar and unfamiliar referents were illustrated pictorially without much difficulty by users. The more visual the referent, the less difficulty the users had in illustrating it. The findings of this study should aid the optimisation of the stereotype production method for user-involved symbol design.     

A linearised pixel-swapping method for mapping rural linear land cover features from fine spatial resolution remotely sensed imagery

Accurate maps of rural linear land cover features, such as paths and hedgerows, would be useful to ecologists, conservation managers and land planning agencies. Such information might be used in a variety of applications (e.g., ecological, conservation and land management applications). Based on the phenomenon of spatial dependence, sub-pixel mapping techniques can be used to increase the spatial resolution of land cover maps produced from satellite sensor imagery and map such features with increased accuracy. Aerial photography with a spatial resolution of 0.25 m was acquired of the Christchurch area of Dorset, UK. The imagery was hard classified using a simple Mahalanobis distance classifier and the classification degraded to simulate land cover proportion images with spatial resolutions of 2.5 and 5 m. A simple pixel-swapping algorithm was then applied to each of the proportion images. Sub-pixels within pixels were swapped iteratively until the spatial correlation between neighbouring sub-pixels for the entire image was maximised. Visual inspection of the super-resolved output showed that prediction of the position and dimensions of hedgerows was comparable with the original imagery. The maps displayed an accuracy of 87%. To enhance the prediction of linear features within the super-resolved output, an anisotropic modelling component was added. The direction of the largest sums of proportions was calculated within a moving window at the pixel level. The orthogonal sum of proportions was used in estimating the anisotropy ratio. The direction and anisotropy ratio were then used to modify the pixel-swapping algorithm so as to increase the likelihood of creating linear features in the output map. The new linear pixel-swapping method led to an increase in the accuracy of mapping fine linear features of approximately 5% compared with the conventional pixel-swapping method.