共查询到20条相似文献，搜索用时 93 毫秒
1.
在高分辨率遥感影像中提取无清晰连续边缘线的道路 总被引：4，自引：0，他引：4
现有的许多道路提取算法均利用道路的外边缘线信息来实现道路的提取,当边缘线清晰连续时,采用这些方法都可以取得很好的提取效果.不过,在高分辨率的城市遥感影像中,常常会存在一些低对比度区域,处于其中的道路边缘线非常之弱,以致难以直接检测出单个的边缘点.如果受到树木、房屋及车辆的干扰,这些原本就很弱的边缘还会发生断裂.通过现有方法提取具有如此边缘线的道路难度很大.本文给出一种旨在解决这一问题的新方法.首先借鉴相位编组原理形成边缘线支持区并对其进行连接;然后利用动态规划方法从支持区中检测出边缘线并对这些线进行平滑;最后连接由边缘线构成的道路段,得出道路提取结果.实验表明,本方法可以较好地提取出无清晰连续边缘线的道路,对于边缘对比度较大的道路则可取得更为令人满意的结果. 相似文献
2.
We present here the examples that show how fusing data from hyperspectral sensors with data from high spatial resolution sensors can enhance overall road detection accuracy. The fusion of hyperspectral and high spatial resolution data combines their superior respective spectral and spatial information. IKONOS (MSS) and Hyperion images were fused using the principal component analysis (PCA) method. The approach for road extraction integrates multiresolution segmentation and object oriented classification. Road extraction is done from an IKONOS (MSS) image and a Hyperion and IKONOS (MSS) merged image and comparisons are made depending on accuracy and quality measures such as completeness and correctness. This article also emphasises the types of roads which are giving better accuracy of extraction after fusion with hyperspectral image. This can vary because of types of material and condition of roads. The methodology was applied on roads of Dehradun, India. 相似文献
3.
Automatic road extraction using high resolution satellite image based on texture progressive analysis and normalized cut method 总被引：1，自引：0，他引：1
J. Senthilnath M. Rajeshwari S. N. Omkar 《Journal of the Indian Society of Remote Sensing》2009,37(3):351-361
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. 相似文献
4.
5.
A. Mohammadzadeh M. J. Valadan Zoej A. Tavakoli 《Journal of the Indian Society of Remote Sensing》2009,37(2):173-184
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. 相似文献
6.
准确提取输电通道内各地物类别是进行输电通道三维建模及交跨分析的基础。为解决目前贵州等多山地区输电通道内道路提取方法稳健性低、效率低的问题,本文通过结合道路特征显著性检测与形态约束的方法对影像中多山地区输电通道内道路的快速准确提取进行了研究。首先,对卫星影像中山区道路特征进行分析,对每个像素分别计算基于道路颜色一致性和结构一致性的像素级显著值;然后,结合显著目标空间先验知识融合显著性检测结果,形成最终道路显著图,初步提取影像中道路目标;最后,分析道路与建筑物等的差异性,基于道路形态一致性定义道路形态约束条件,通过制定约束规则最终实现道路的准确提取。结果表明,该方法对于不同弯曲程度、粘连程度及影像对比度的道路都能实现快速准确提取,提取完整度、正确率、质量及耗时平均分别为97.5%、97.0%、95.6%、0.515 s。该方法稳健性高,可以快速、准确提取各种情况下道路,在输电线路工程实际中有很好的应用前景。 相似文献
7.
An Integrated Multistage Framework for Automatic Road Extraction from High Resolution Satellite Imagery 总被引：1，自引：0，他引：1
T. T. Mirnalinee Sukhendu Das Koshy Varghese 《Journal of the Indian Society of Remote Sensing》2011,39(1):1-25
Automated procedures to rapidly identify road networks from high-resolution satellite imagery are necessary for modern applications
in GIS. In this paper, we propose an approach for automatic road extraction by integrating a set of appropriate modules in
a unified framework, to solve this complex problem. The two main properties of roads used are: (1) spectral contrast with
respect to background and (2) locally linear path. Support Vector Machine is used to discriminate between road and non-road
segments. We propose a Dominant singular Measure (DSM) for the task of detecting linear (locally) road boundaries. This pair
of information of road segments, obtained using Probabilistic SVM (PSVM) and DSM, is integrated using a modified Constraint
Satisfaction Neural Network. Results of this integration are not satisfactory due to occlusion of roads, variation of road
material, and curvilinear pattern. Suitable post-processing modules (segment linking and region part segmentation) have been
designed to address these issues. The proposed non-model based approach is verified with extensive experimentations and performance
compared with two state-of-the-art techniques and a GIS based tool, using multi-spectral satellite images. The proposed methodology
is robust and shows superior performance (completeness and correctness are used as measures) in automating the process of
road network extraction. 相似文献
8.
根据高分辨率遥感影像中城镇道路的特点,提出了一种基于特征点的道路信息提取方法。首先,对影像进行增强处理并选取感兴趣的子区域,利用改进的分水岭分割理论和阈值选择算法,结合八邻域检测方法得到道路的特征点;然后,利用回归分析方法在一定的坐标系统下得到每条道路的回归方程,根据端点坐标信息得到道路信息图;最后,利用数学形态学算法获取道路骨架图。结果表明,本方法能够精确有效地提取高分辨率遥感影像的城镇道路信息。 相似文献
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10.
Accurate and current road network data is fundamental to land management and emergency response, yet challenging to produce for unpaved roads in rural and forested regions using traditional cartographic approaches. Automatic extraction of roads from satellite imagery using deep learning is a promising alternative gaining increasing attention, however most efforts have focused on urban paved roads and used very high spatial resolution imagery, which is less frequently available for rural regions. Additionally, road extraction routines still struggle to produce a fully-connected, vectorized road network. In this study covering a large forested area in Western Canada, we developed and evaluated a routine to automatically extract unpaved road pixels using a convolutional neural network (CNN), and then used the CNN outputs to update a pre-existing government road network and evaluate if and how it would change. To cover the large spatial extent mapped in this study, we trained the routine using moderately high-resolution satellite imagery from the RapidEye constellation and a ground-truth dataset collected with smartphones by organizations already operating and driving in the region. Performance of the road extraction was comparable to results achieved by others using very high-resolution imagery; recall accuracy was 89–97%, and precision was 85–91%. Using our approach to update the pre-existing road network would result in both removals and additions to the network, totalling over 1250 km, or about 20 % of the roads previously in the network. We discuss how road density estimates in the study area would change using this updated network, and situate these changes within the context of ongoing efforts to conserve grizzly bears, which are listed as a Threatened species in the region. This study demonstrates the potential of remote sensing to maintain current and accurate rural road networks in dynamic forest landscapes where new road construction is prevalent, yet roads are also frequently de-activated, reclaimed or otherwise not maintained. 相似文献
11.
道路综合特征下高分辨率遥感影像的提取 总被引：1，自引：0，他引：1
针对在高分辨率遥感影像中如何提高道路信息提取的准确度和信息量这一问题,通过对影像光谱和纹理特征的分析,将影像特征按照2种光谱特征和3种纹理特征进行分类,进而改善传统的图像分割方法,选择灰度级数和像素对的相对方向、距离和窗口大小作为参数,再通过灰度共生矩阵运算获取影像的纹理信息,通过对这些纹理特征的综合比较分析,最后确定角二阶矩、熵和对比度作为道路纹理特征统计量;再通过对图像像元分析比较,将图像像元标准差和灰度均值作为道路信息提取的光谱特征;在对道路综合特征分析基础上,再通过对遥感图像几何特征分析,最后利用数学形态学的开运算、闭运算、腐蚀、细化等模型算法对遥感图像进行精细化处理,得到道路提取较好的结果。该方法可用于复杂路况的道路信息提取。 相似文献
12.
面向对象的形态学建筑物指数及其高分辨率遥感影像建筑物提取应用 总被引：3，自引：3，他引：0
高分辨率遥感影像建筑物提取是摄影测量与遥感领域的一个热门研究主题。本文综合利用影像分割、基于图的数学形态学top-hat重建技术,提出了面向对象的形态学建筑物指数OBMBI,并将其应用于高分辨率遥感影像建筑物提取。首先,建立像素-对象-图节点的双向映射关系;然后,基于图的白top-hat重建和上述映射关系来构建OBMBI图像;接着,对该OBMBI图像二值化、矢量化以获取建筑物多边形;最后,对结果进行后处理优化。使用一景航空、一景卫星全色影像对本文方法和PanTex方法进行性能测试。试验表明,本文方法的建筑物提取精度显著的优于PanTex方法。其中,本文方法平均比PanTex方法的正确率高9.49%、完整率高11.26%、质量高14.11%。 相似文献
13.
针对目前利用高分遥感数据提取农村道路的研究与应用少,提取结果精准度不够的问题,提出了结合空洞卷积和ASPP（Atrous Spatial Pyramid Pooling）结构的改进全卷积农村道路提取网络模型DC-Net（Dilated Convolution Network）。该模型基于全卷积的编解码结构来提取道路深度特征信息,同时针对农村道路细长的特点,在解编码层之间加入了以空洞卷积为基础的ASPP（Atrous Spatial Pyramid Pooling）结构来提取道路的多尺度特征信息,在不牺牲特征空间分辨率的同时扩大了特征感受野FOV（Field-of-View）,从而提高细窄农村道路的识别率。以长株潭城市群郊区部分区域为试验对象,以高分二号国产卫星遥感影像为实验数据,将本文提出的方法与经典的几种全卷积网络方法进行实验结果对比分析。实验结果表明：（1）本文所提出的道路提取模型DC-Net在农村道路的提取上具有可行性,整体提取平均精度达到98.72%,具有较高的提取精度;（2）对比几种经典的全卷积网络模型在农村道路提取上的效果,DC-Net在农村道路提取的精度和连结性、以及树木和阴影的遮挡方面,均表现出了较好的提取结果;（3）本文提出的改进全卷积网络道路提取模型能够有效地提取高分辨率遥感影像中农村道路的特征信息,总体提取效果较好,为提高基于国产高分影像的农村道路提取精度提供了一种新的思路和方法。 相似文献
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Poonam S. Tiwari H. Pande Ashwini Kumar Pandey 《Journal of the Indian Society of Remote Sensing》2009,37(2):223-231
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. 相似文献
16.
Hatice Catal Reis Bülent Bayram Salih Bozkurt Abdulkadir Ince Nusret Demir Dursun Zafer Seker 《Journal of the Indian Society of Remote Sensing》2018,46(8):1223-1232
This study presents an extended shoreline detection approach from pansharpened images of Turkish RASAT satellite which covers red, green and blue bands of the optical spectrum with 15 m ground resolution and panchromatic band with 7.5 m spatial resolution. The Lake Ercek of Turkey has been selected as the study area, which is a tectonic lake and home to a variety of water birds. The satellite images of the lake taken in 2013 and 2014 were considered for analysis. The proposed shoreline extraction system consists of a sequence of image processing steps in which simple linear clustering (SLIC) and particle swarm optimization (PSO) are the main components. SLIC was used to create superpixels that form basis for object-based image analysis while PSO was employed for classifying objects into corresponding classes. The resulting images still contained unwanted artefacts; therefore, a post-processing step was performed to improve the accuracy of segmentation by applying thresholding, morphological processing, and manual editing for noise removal. The proposed framework was applied on two temporal RASAT images to test the variations of defined parameter settings. The success of the proposed system was to obtain shorelines with satisfying accuracy without using NIR band. Finally, the extracted shorelines were vectorised and compared with manually digitized shorelines from pansharpened satellite images for accuracy assessment. 相似文献
17.
道路是城区地理空间信息中最重要的基础设施之一,从高分辨影像中自动、快速的提取道路特征,是快速更新城市道路网信息的重要途径。文中在分析道路基本特征的基础上,选择基于自适应结构元素的形态分析算法提取初始道路区域;引入面积和长宽比等形状指数,得到较精确的道路信息;最后,采用Hilditch细化算法,并进行优化处理。实验证明,该道路提取过程中无需人工设置参数,且能够得到具有较高完整性和正确性的道路中心线。 相似文献
18.
This paper introduces a novel technique for object detection using genetic algorithms and morphological processing. The method employs a kind of object oriented structure element, which is derived by genetic algorithms. The population of morphological filtersis iteratively evaluated according to a statistical performance index corresponding to object extraction ability, and evolves into an optimal structuring element using the evolution principles of genetic search. Experimental results of road extraction from high resolution satellite images are presented to illustrate the merit and feasibility of the proposed method. 相似文献
19.
LIBin PANLi 《地球空间信息科学学报》2004,7(3):193-197
This paper introduces a novel technique for object detection using genetic algorithms and morphological processing. The method employs a kind of object oriented structure element, which is derived by genetic algorithms. The population of morphological filters is iteratively evaluated according to a statistical performance index corresponding to object extraction ability, and evolves into an optimal structuring element using the evolution principles of genetic search. Experimental results of road extraction from high resolution satellite images are presented to illustrate the merit and feasibility of the proposed method. 相似文献
20.
本文介绍了加拿大新不伦瑞克大学（UNB）高级地球空间信息图像处理实验室（CRC-AGIP实验室）和大地测量与地球空间信息工程系（GGE）开发的一些影像处理技术。通过创新性地利用高分辨率遥感光学影像的各种特性,这些技术解决了一些摄影测量和遥感中的重要问题并实现了一些创造新的应用。所介绍的技术包括：自动影像融合（UNB-PanSharp）、卫星影像在线制图、街景技术、单景卫星影像移动车辆检测、有监督影像分割、平滑区域影像匹配及不同视角影像变化检测。 相似文献