Automatic main road extraction from high resolution satellite imageries by means of particle swarm optimization applied to a fuzzy-based mean calculation approach

Manual extraction of road network by human operator is an expensive and time-consuming procedure. Alternatively, automation of the extraction process would be a great advancement. For this purpose, an automatic method is proposed to extract roads from high resolution satellite images. In this study, using few samples from road surface, a particle swarm optimization is applied to a fuzzy-based mean calculation system to obtain road mean values in each band of high resolution satellite colour images. Then, the images are segmented using the calculated mean values from the fuzzy system. Optimizing the fuzzy cost function by particle swarm optimization enables the fuzzy approach to be the best mean value of road with sub-grey level precision. Initially, this method was applied to simulated images where the calculated mean values are consistent with the hypothetic mean values. Application of the method to IKONOS satellite images has shown a prospective outcome for automatic road extraction. Mathematical morphology is subsequently used to extract an initial main road centreline from the segmented image. Then, small redundant segments are automatically removed. The quality of the extracted road centreline indicates the effectiveness of the proposed approach.     

基于直线和区域特征的遥感影像线状目标检测

针对高分辨率航空遥感影像中线状目标的特点,提出一种结合区域和直线特征识别线状目标的方法。在基于标记点分水岭变换进行初始分割的基础上,利用关于目标的知识和区域邻接图(RAG)对感兴趣区域进行合并,得到最终检测结果。实验结果表明,本文方法可以有效地从遥感影像中提取线状目标。     

基于面向对象方法的高分辨率遥感影像道路提取方法研究

随着遥感影像分辨率的不断提高,基于高分辨率遥感影像的目标自动提取逐步成为研究热点。本文采用面向对象的图像分析方法,基于Ecognition遥感图像处理平台,对IKONOS影像进行道路提取实验,重点对图像分割方案、道路提取规则、后处理方法等进行探讨。     

Automatic urban road extraction using airborne laser scanning/altimetry and high resolution satellite data

Automatic road extraction from remotely sensed images has been an active research in urban area during last few decades. But such study becomes difficult in urban environment due to mix of natural and man-made features. This research explores methodology for semiautomatic extraction of urban roads. An integrated approach of airborne laser scanning (ALS) altimetry and high-resolution data has been used to extract road and differentiate them from flyovers. Object oriented fuzzy rule based approach classifies roads from high resolution satellite images. Complete road network is extracted with the combination of ALS and high-resolution data. The results show that an integration of LiDAR data and IKONOS data gives better accuracy for automatic road extraction. The method was applied on urban area of Amsterdam, The Netherlands.     

结合尺度空间的面向对象高分辨率影像城市道路提取

基于遥感影像的城市道路提取对于城市建设、规划和地图更新等有重要意义。针对高分辨率遥感影像城市道路网的复杂性,结合尺度空间思想提出一种面向对象的城市道路自动提取算法。在此基础上,使用Canny算子获取像元簇梯度图,并进行标记分水岭分割得到区域对象;建立城市道路与几何、光谱特征相关的道路规则,从分割结果中筛选出道路区域对象;使用形态学方法提取道路区域的骨架,并对骨架进行连接、光滑等后处理,最后输出道路网提取结果。实验结果表明,该方法用于复杂城市道路的高精度自动提取,对城市道路网更新有一定参考意义。     

基于标点随机过程的遥感影像道路提取

在分析贝叶斯方法用于遥感影像目标提取技术的基础上．基于标点随机过程方法，利用线状地物的整体几何约束和地物之间的空间结构及相关关系对目标构建数学模型．提取线状地物．并以道路网的自动提取为例，详细阐述了此算法。     

兼顾连通性的U-Net网络高分辨率遥感影像道路提取

遥感影像道路提取结果中的断线一方面降低了提取精度,另一方面影响了道路形态完整性,使得提取结果不能直接应用于空间决策与分析。本文基于U-Net网络在高分辨率遥感影像道路提取时全局特征表达的优势,提出一种兼顾连通性的道路断线修复方法完善U-Net网络局部特征表达的劣势。首先,利用数据增强和扩充数据量后的样本数据作为U-Net网络的输入以此训练模型并进行最优模型的道路提取;然后,对提取结果中出现的道路断线以三次多项式曲线拟合的形式进行优化处理。实验表明,与相近网络比较,本文道路提取的精度和形态完整性有了明显的提高,查准率为86.25%,查全率为85.50%,F1-score达到了85.87%。其成果数据能直接地应用于地理决策分析,特别有利于灾后的路径规划,本文提出的方法对道路、电网、轨道、河流等线性地物分类结果中出现类似断线问题具有一定的参考意义。     

Object-Based Rule Sets and Its Transferability for Building Extraction from High Resolution Satellite Imagery

With the advent of high spatial resolution satellite imagery, automatic and semiautomatic building extractions have turned into one of the outstanding research topics in the field of remote sensing and machine vision. To this date, various algorithms have been presented for extracting the buildings from satellite images. Such methods lend their bases to diverse criteria such as radiometric, geometric, edge detection, and shadow. In this paper, a novel object based approach has been proposed for automatic and robust detections as well as extraction of the building in high spatial resolution images. To fulfill this, we simultaneously made use of both stable and variable features. While the former can be derived from inherent characteristics of the buildings, the latter is extracted using a feature analysis tool. In addition, a novel perspective has been recommended to boost the automation degree of the segmentation part in the object based analysis of remote sensing imagery. The proposed method was applied to a QuickBird imagery of an urban area in Isfahan city and the results of the quantitative evaluation demonstrated that the proposed method could yield promising results. Moreover, in another section of this study, for assessing the algorithm transferability, the rule set was implemented to a part of the WorldView image of Yazd city, proving that the proposed approach is capable of transferability in different types of case studies.     

Automatic road extraction using high resolution satellite image based on texture progressive analysis and normalized cut method

In this paper the approach for automatic road extraction for an urban region using structural, spectral and geometric characteristics of roads has been presented. Roads have been extracted based on two levels: Pre-processing and road extraction methods. Initially, the image is pre-processed to improve the tolerance by reducing the clutter (that mostly represents the buildings, parking lots, vegetation regions and other open spaces). The road segments are then extracted using Texture Progressive Analysis (TPA) and Normalized cut algorithm. The TPA technique uses binary segmentation based on three levels of texture statistical evaluation to extract road segments where as, Normalized cut method for road extraction is a graph based method that generates optimal partition of road segments. The performance evaluation (quality measures) for road extraction using TPA and normalized cut method is compared. Thus the experimental result show that normalized cut method is efficient in extracting road segments in urban region from high resolution satellite image.     

空间信息感知语义分割模型的高分辨率遥感影像道路提取

道路信息自动化提取已经成为遥感领域热门的研究方向,而基于深度学习的遥感影像道路信息提取方法已经取得了许多成果。但由于受到网络中卷积和池化等操作的影响,基于深度学习的道路提取方法存在着空间特征和地物细节信息丢失等问题,造成许多误提现象。针对此问题,本文设计了一种改进的道路提取语义分割网络模型,该网络以改进的ResNet网络为主体,并引入坐标卷积和全局信息增强模块,用于增强空间信息和全局上下文信息的感知能力,突出道路边缘特征进而确保道路分类的精确性。本文方法在公开道路数据集和高分数据集上获得了显著的提取效果,与其它方法相比取得了明显提高;并且,在一定程度上减少了树木、建筑阴影等自然场景因素遮挡的影响,可以完整准确地提取出道路;此外,模型对多尺度道路也可以实现有效地提取。     

结合对象关系特征的高分辨率卫星影像建筑物识别方法

基于面向对象特征影像分析的思想,提出了一种结合建筑物和阴影对象邻近关系特征的建筑物识别提取方法。在多尺度影像 分割的基础上,利用对象的光谱和形状等特征,建立简单的分类决策树,提取粗略的建筑物候选区和相对准确的阴影区。计算相邻 近阴影对象和建筑物对象的关系特征,建立简单的知识规则,即可从建筑物候选区中消除广场等噪音,获得准确有效的建筑物目标 信息。通过QuickBird卫星影像的实验,证明了该方法在高分辨率卫星影像建筑物目标识别中具有相当的适用性和准确性。     

迁移学习支持下的遥感影像对象级分类样本自动选择方法

面向遥感大范围应用的目标,自动化程度仍是遥感影像分类面临的重要问题,样本的人工选择难以适应当前土地覆盖信息自动化提取的实际应用需求。为了构建一套基于先验知识的遥感影像全自动分类流程,本文将空间信息挖掘技术引入到遥感信息提取过程中,提出了一种面向遥感影像对象级分类的样本自动选择方法。该方法通过变化检测将不变地物标示在新的目标影像上,并将过去解译的地物类别知识迁移至新的影像上,建立新的特征与地物关系,从而完成历史专题数据辅助下目标影像的自动化的对象级分类。实验结果表明,在已有历史专题层的图斑知识指导下,该方法能有效地自动选择适用于新影像分类的可靠样本,获得较好的信息提取效果,提高了对象级分类的效率。     

融合高分影像和LiDAR数据的城市道路提取

为避免由于城市道路复杂及树木建筑的阴影遮挡导致从遥感影像中提取道路信息不准确的问题,本文采用高分影像和Li-DAR数据相融合的方法实现城市道路的提取,并使用一种基于最小面积外接矩形(MABR)的后处理改进方法进行完善.首先对试验区进行数据配准;然后应用FNEA算法进行图像分割,并使用随机森林分类法进行分类,将影像融合和...     

高分辨率遥感影像建筑物分级提取

高分辨率遥感影像建筑物信息自动提取是遥感应用研究中的一个热点问题,但由于受到成像条件不同、背景地物复杂、建筑物类型多样等多个因素的影响使得建筑物的自动提取仍然十分困难。为此,在综合考虑影像光谱、几何与上下文特征的基础上,提出了一种基于面向对象与形态学相结合的高分辨率遥感影像建筑物信息分级提取方法。该方法首先利用影像的多尺度及多方向Gabor小波变换结果提取建筑物特征点;然后采用面向对象的思想构建空间投票矩阵来度量每一个像素点属于建筑物区域的概率,从而提取出建筑物区域边界;最后在提取的建筑物区域内应用形态学建筑物指数实现建筑物信息的自动提取。实验结果表明,本文方法能够高效、高精度地完成复杂场景下的建筑物信息提取,且提取结果的正确性和完整性都优于效果较好的PanTex算法。     

多特征融合的高分辨率影像建筑物变化检测

为充分发挥遥感影像中各特征的优势,提高遥感影像建筑物变化检测精度,基于面向对象的分析方法,提出了一种基于模糊集合的证据理论特征信息融合的变化检测方法。首先,在影像分割的基础上,利用变化矢量分析法分别计算前后时相对应对象的光谱、纹理特征差异及形态学建筑物指数差异;然后,以Sigmoid函数作为隶属度函数,计算对象属于变化类和非变化类的隶属度并以之构建证据理论所需的基本概率分配函数;最后,利用证据理论对多种特征进行融合并通过规则判定得到建筑物变化区域。利用不同地区影像的试验结果表明,该方法能够有效融合影像的多种特征,提高建筑物变化检测的精度。     

Automatic Registration Between Low-Altitude LiDAR Point Clouds and Aerial Images Using Road Features

Among the many means of acquiring surface information, low-altitude light detection and ranging (LiDAR) systems (e.g., unmanned aerial vehicle LiDAR, UAV-LiDAR) have become an important approach to accessing geospatial information. Considering the lower level of hardware technology in low-altitude LiDAR systems compared to that in airborne LiDAR, and the greater flexibility in-flight, registration procedures must be first performed to facilitate the fusion of laser point data and aerial images. The corner points and edges of buildings are frequently used for the automatic registration of aerial imagery with LiDAR data. Although aerial images and LiDAR data provide powerful support for building detection, adaptive edge detection for all types of building shapes is difficult. To deal with the weakness of building edge detection and reduce matching-related computation, the study presents a novel automatic registration method for aerial images, with LiDAR data, on the basis of main-road information in urban areas. Firstly, vector road centerlines are extracted from raw LiDAR data and then projected onto related aerial images with the use of coarse exterior orientation parameters (EOPs). Secondly, the corresponding image road features of each LiDAR vector road are determined using an improved total rectangle-matching approach. Finally, the endpoints of the conjugate road features obtained from the LiDAR data and aerial images are used as ground control points in space resection adjustment to refine the EOPs; an iterative strategy is used to obtain optimal matching results. Experimental results using road features verify the feasibility, robustness and accuracy of the proposed approach.     

基于多种子点模糊连接度的SPOT影像道路提取

结合模糊连接度理论和SPOT影像上道路的表现特性提出了主干道路半自动提取的方法。首先,对原始影像进行去噪滤波处理,再人工选取能够代表道路特性的种子点,计算出各个像素点相对种子点的模糊连接度,将模糊连接度值大于一定阈值的像素点提取出来,从而得到道路线的支持区域;接着对二值道路域细化抽去得到道路中心线;实验证明,该方法可以取得较好的效果。     

Stereo analysis of high-resolution SAR images for building height estimation in cases of orthogonal aspect directions

SAR stereo image analysis for 3D information extraction is mostly carried out based on imagery taken under same-side or opposite-side viewing conditions. For urban scenes in practice stereo is up to now usually restricted to the first configuration, because increasing image dissimilarity connected with rising illumination direction differences leads to a lack of suitable features for matching, especially in the case of low or medium resolution data. However, due to two developments SAR stereo from arbitrary viewing conditions becomes an interesting option for urban information extraction. The first one is the availability of airborne sensor systems, which are capable of more flexible data acquisition in comparison to satellite sensors. This flexibility enables multi-aspect analysis of objects in built-up areas for various kinds of purpose, such as building recognition, road network extraction, or traffic monitoring. The second development is the significant improvement of the geometric resolution providing a high level of detail especially of roof features, which can be observed from a wide span of viewpoints. In this paper, high-resolution SAR images of an urban scene are analyzed in order to infer buildings and their height from the different layover effects in views taken from orthogonal aspect angles. High level object matching is proposed that relies on symbolic data, representing suitable features of urban objects. Here, a knowledge-based approach is applied, which is realized by a production system that codes a set of suitable principles of perceptual grouping in its production rules. The images are analyzed separately for the presence of certain object groups and their characteristics frequently appearing on buildings, such as salient rows of point targets, rectangular structures or symmetries. The stereo analysis is then accomplished by means of productions that combine and match these 2D image objects and infer their height by 3D clustering. The approach is tested using real SAR data of an urban scene.     

像元与对象特征融合的高分辨率遥感影像道路中心线提取

提出了一种融合像元-多尺度对象级特征的高分辨率遥感影像道路中心线提取方法。首先在像素级上提取影像的纹理和形状结构特征,在构建的多尺度分割集影像上提取对象的区域光谱特征。然后,将像元级特征与多尺度对象特征进行决策级融合,完成道路网的粗提取。最后,结合本文所提出的非道路区域自动去除算法和张量投票算法,实现道路中心线的精提取。不同场景、不同分辨率数据下开展的试验结果表明,该方法可有效改善传统道路提取方法易产生的"盐噪声"和非道路地物粘连现象。     

Investigation of SVM and Level Set Interactive Methods for Road Extraction from Google Earth Images

Currently, methods of extracting spatial information from satellite images are mainly based on visual interpretations and drawing the consequences by human factor, which is both costly and time consuming. A large volume of data collected by satellite sensors, and significant improvement in spatial and spectral resolution of these images require the development of new methods for optimal use of these data in order to produce rapid economic and updating road maps. In this study, a new automatic method is proposed for road extraction by integrating the SVM and Level Set methods. The estimated probability of classification by SVM is used as input in Level Set Method. The average of completeness, correctness, and quality was 84.19, 88.69 and 76.06% respectively indicate high performance of proposed method for road extraction from Google Earth images.