Shadow detection in very high spatial resolution aerial images: A comparative study

Automatic shadow detection is a very important pre-processing step for many remote sensing applications, particularly for images acquired with high spatial resolution. In complex urban environments, shadows may occupy a significant portion of the image. Ignoring these regions would lead to errors in various applications, such as atmospheric correction and classification. To better understand the radiative impact of shadows, a physical study was conducted through the simulation of a synthetic urban canyon scene. Its results helped to explain the most common assumptions made on shadows from a physical point of view in the literature. With this understanding, state-of-the-art methods on shadow detection were surveyed and categorized into six classes: histogram thresholding, invariant color models, object segmentation, geometrical methods, physics-based methods, unsupervised and supervised machine learning methods. Among them, some methods were selected and tested on a large dataset of multispectral and hyperspectral airborne images with high spatial resolution. The dataset chosen contains a large variety of typical occidental urban scenes. The results were compared based on accurate reference shadow masks. In these experiments, histogram thresholding on RGB and NIR channels performed the best with an average accuracy of 92.5%, followed by physics-based methods, such as Richter’s method with 90.0%. Finally, this paper analyzes and discusses the limits of these algorithms, concluding with some recommendations for shadow detection.     

Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours

To present a new method for building boundary detection and extraction based on the active contour model, is the main objective of this research. Classical models of this type are associated with several shortcomings; they require extensive initialization, they are sensitive to noise, and adjustment issues often become problematic with complex images. In this research a new model of active contours has been proposed that is optimized for the automatic building extraction. This new active contour model, in comparison to the classical ones, can detect and extract the building boundaries more accurately, and is capable of avoiding detection of the boundaries of features in the neighborhood of buildings such as streets and trees. Finally, the detected building boundaries are generalized to obtain a regular shape for building boundaries. Tests with our proposed model demonstrate excellent accuracy in terms of building boundary extraction. However, due to the radiometric similarity between building roofs and the image background, our system fails to recognize a few buildings.     

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 road change detection and GIS updating from high spatial remotely-sensed imagery

This paper presents a framework for road network change detection in order to update the Canadian National Topographic DataBase (NTDB). The methodology has been developed on the basis of road extraction from IRS-pan images (with a 5.8 m spatial resolution) by using a wavelet approach. The feature matching and conflation techniques are used to road change detection and updating. Elementary experiments have showed that the proposed framework could be used for developing an operational road database updating system.     

Automatic Road Change Detection and GIS Updating from High Spatial Remotely-Sensed Imagery

This paper presents a tramework for road network change detectlon In order to upctate the Canadian National Topographic DataBase (NTDB). The methodology has been developed on the basis of road extraction from IRS-pan images (with a 5.8 m spatial resolution) by using a wavelet approach. The feature matching and conflation techniques are used to road change detection and updating. Elementary experiments have showed that the proposed framework could be used for developing an operational road database updating system.     

Automated metric characterization of urban structure using building decomposition from very high resolution imagery

Classification approaches for urban areas are mostly of qualitative and semantic nature. They produce interpreted classes similar to those from land cover and land use classifications. As a complement to those classes, quantitative measures directly derived from the image could lead to a metric characterization of the urban area. While these metrics lack of qualitative interpretation they are able to provide objective measure of the urban structures.Such quantitative measures are especially important in rapidly growing cities since, beside of the growth in area, they can provide structural information for specific areas and detect changes. Rustenburg, which serves as test area for the present study, is amongst the fastest growing cities in South Africa. It reveals a heterogeneous face of housing and building structures reflecting social and/or economic differences often linked to the spatial distribution of industrial and local mining sites. Up to date coverage with aerial photographs is provided by aerial surveys in regular intervals. Also recent satellite systems provide imagery with suitable resolution. Using such set of very high resolution images a fully automated algorithm has been developed which outputs metric classes by systematically combining important measures of building structure. The measurements are gained by decomposition of buildings directly from the imagery and by using methods from mathematical morphology. The decomposed building objects serve as basis for the computation of grid statistics. Finally a systematic combination of the single features leads to combined metrical classes.For the dominant urban structures verification results indicate an overall accuracy of at least 80% on the single feature level and 70% for the combined classes.     

高分辨率遥感影像建筑区域局部几何特征提取

及时准确地获取城市建筑区域的空间分布及其变化信息对于城市规划、空间地理数据库建设及区域社会经济分析具有重要意义。本文提出一种基于多尺度Gabor变换和感知聚类方法即张量投票TV (Tensor Voting)相结合的自适应局部几何不变特征检测方法,并将其应用于高空间分辨率遥感影像建筑区域提取。首先,考虑到高分辨率遥感影像复杂的几何结构特征,使用Gabor滤波器组对影像进行多尺度多方向变换检测奇异性特征。然后,在感知聚类框架下,根据张量投票理论将不同方向子带系数位置编码为相应的二阶对称方向张量,为了突出影像几何特征,对不同尺度、不同方向子带中任意像素位置方向张量使用滤波器响应系数加权并求和完成多尺度特征融合。再次,对张量特征分解得到点结构与线结构显著性图并使用非极大抑制提取相应角点和曲线等局部几何特征,同时生成约束准则筛选角点以确定建筑物坐标。最后,利用概率密度估计结合局部角点特征生成全局概率密度场描述影像中像素从属于建筑目标的概率,并使用最大类间方差法(Otsu)阈值分割自动提取居民地多边形区域。使用分辨率分别为0.49 m、0.98 m的Google Earth及0.8 m的高分二号等影像数据集进行实验,实验结果表明本文方法相对于已有的Harris和HSCD点检测算法,在建筑区域提取质量上(Quality)上分别提高了4.79%,5.96%;1.47%,3.76%和1.91%,4.08%。     

基于光谱和空域信息的城区变化检测方法研究

目前的变化检测研究主要集中在利用中、小比例尺遥感图像进行自然环境等方面的变化检测,如草场的季节性变化、灾害检测、植被分布变化、土地使用规划等。然而随着城市的快速发展,为了满足城区管理规划中对道路、房屋等变化细节进行分析的需求,并由于高分辨率卫星诸如Ikonos,Quickbird等的出现,我们有必要并且有可能开发出一套实用、有效的、可靠的城区自动变化检测系统。针对城市区域的复杂性及其高分辨率卫星影像的配准误差问题,本文提出一种通过模糊逻辑结合光谱特征和空域特征的城区变化检测方法,以期望减小图像整体及局部配准误差对变化检测精度的影响。     

高分遥感影像的围填海变化图斑自动提取方法

针对已有的围填海图斑提取方法精度不高、普适性不强、自动提取结果不理想等问题,该文提出了通过构建归一化差异水体指数(NDWI)进行围填海变化图斑自动提取的方法。以高分辨率QuickBird影像和HJ-1卫星影像为数据源,首先,根据研究区的用海类型进行5种易混淆地物的波谱特征分析;然后,根据水体与非水体的光谱特征差异,构建2009、2011年两个时相的NDWI指数;最后,将两时相NDWI指数影像进行空间相减,设置判断阈值,识别围填海变化图斑,并以目视提取结果作为依据验证其自动提取效果。对比分析结果表明:利用该文构建的两期NDWI指数可以将大部分围填海区域准确、自动地探测出来,可以将该方法纳入到沿海地区围填海变化监测的业务中。     

Development of fuzzy rule-based parameters for urban object-oriented classification using very high resolution imagery

Urban areas consist of spectrally and spatially heterogeneous features. Advanced information extraction techniques are needed to handle high resolution imageries in providing detailed information for urban planning applications. This study was conducted to identify a technique that accurately maps impervious and pervious surfaces from WorldView-2 (WV-2) imagery. Supervised per-pixel classification algorithms including Maximum Likelihood and Support Vector Machine (SVM) were utilized to evaluate the capability of spectral-based classifiers to classify urban features. Object-oriented classification was performed using supervised SVM and fuzzy rule-based approach to add spatial and texture attributes to spectral information. Supervised object-oriented SVM achieved 82.80% overall accuracy which was the better accuracy compared to supervised per-pixel classifiers. Classification based on the proposed fuzzy rule-based system revealed satisfactory output compared to other classification techniques with an overall accuracy of 87.10% for pervious surfaces and an overall accuracy of 85.19% for impervious surfaces.     

Comparing supervised and unsupervised multiresolution segmentation approaches for extracting buildings from very high resolution imagery

Although multiresolution segmentation (MRS) is a powerful technique for dealing with very high resolution imagery, some of the image objects that it generates do not match the geometries of the target objects, which reduces the classification accuracy. MRS can, however, be guided to produce results that approach the desired object geometry using either supervised or unsupervised approaches. Although some studies have suggested that a supervised approach is preferable, there has been no comparative evaluation of these two approaches. Therefore, in this study, we have compared supervised and unsupervised approaches to MRS. One supervised and two unsupervised segmentation methods were tested on three areas using QuickBird and WorldView-2 satellite imagery. The results were assessed using both segmentation evaluation methods and an accuracy assessment of the resulting building classifications. Thus, differences in the geometries of the image objects and in the potential to achieve satisfactory thematic accuracies were evaluated. The two approaches yielded remarkably similar classification results, with overall accuracies ranging from 82% to 86%. The performance of one of the unsupervised methods was unexpectedly similar to that of the supervised method; they identified almost identical scale parameters as being optimal for segmenting buildings, resulting in very similar geometries for the resulting image objects. The second unsupervised method produced very different image objects from the supervised method, but their classification accuracies were still very similar. The latter result was unexpected because, contrary to previously published findings, it suggests a high degree of independence between the segmentation results and classification accuracy. The results of this study have two important implications. The first is that object-based image analysis can be automated without sacrificing classification accuracy, and the second is that the previously accepted idea that classification is dependent on segmentation is challenged by our unexpected results, casting doubt on the value of pursuing ‘optimal segmentation’. Our results rather suggest that as long as under-segmentation remains at acceptable levels, imperfections in segmentation can be ruled out, so that a high level of classification accuracy can still be achieved.     

Estimating very high resolution urban surface temperature using a spectral unmixing and thermal mixing approach

Land surface temperature (LST) plays a critical role in characterizing energy exchanges of the Earth's surface and atmosphere. Recent advances in thermal infrared (TIR) remote sensing technology enable the emergence of airborne very-high-resolution (VHR) TIR sensors to identify detailed LST distribution for environmental, geological and urban applications. However, the usage of airborne VHR TIR data may be limited by its high cost, long acquisition period, extensive data processing, etc. A cost-effective alternative could be VHR LST estimation. We proposed a physically based method, referred to as the VHR spectral unmixing and thermal mixing (VHR-SUTM) approach, to estimate LST at the meter level. Particularly, considering both spectral and thermal properties, spectral unmixing was employed to estimate fractional urban compositions for a comprehensive representation of heterogeneous urban surfaces. Further, VHR LST was modeled as a summation of the thermal features of representative urban compositions weighted by their respective abundances. Results suggest a high agreement between the resampled VHR LST estimates and the retrieved LSTs. With relatively high estimation accuracy (RMSE of 2.02 K and MAE of 1.51 K), the VHR-SUTM technique could serve as a promising and practical method for various applications in urban and environment studies.     

Object-based habitat mapping using very high spatial resolution multispectral and hyperspectral imagery with LiDAR data

This study investigated the combined use of multispectral/hyperspectral imagery and LiDAR data for habitat mapping across parts of south Cumbria, North West England. The methodology adopted in this study integrated spectral information contained in pansharp QuickBird multispectral/AISA Eagle hyperspectral imagery and LiDAR-derived measures with object-based machine learning classifiers and ensemble analysis techniques. Using the LiDAR point cloud data, elevation models (such as the Digital Surface Model and Digital Terrain Model raster) and intensity features were extracted directly. The LiDAR-derived measures exploited in this study included Canopy Height Model, intensity and topographic information (i.e. mean, maximum and standard deviation). These three LiDAR measures were combined with spectral information contained in the pansharp QuickBird and Eagle MNF transformed imagery for image classification experiments. A fusion of pansharp QuickBird multispectral and Eagle MNF hyperspectral imagery with all LiDAR-derived measures generated the best classification accuracies, 89.8 and 92.6% respectively. These results were generated with the Support Vector Machine and Random Forest machine learning algorithms respectively. The ensemble analysis of all three learning machine classifiers for the pansharp QuickBird and Eagle MNF fused data outputs did not significantly increase the overall classification accuracy. Results of the study demonstrate the potential of combining either very high spatial resolution multispectral or hyperspectral imagery with LiDAR data for habitat mapping.     

Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery

Many municipal activities require updated large-scale maps that include both topographic and thematic information. For this purpose, the efficient use of very high spatial resolution (VHR) satellite imagery suggests the development of approaches that enable a timely discrimination, counting and delineation of urban elements according to legal technical specifications and quality standards. Therefore, the nature of this data source and expanding range of applications calls for objective methods and quantitative metrics to assess the quality of the extracted information which go beyond traditional thematic accuracy alone. The present work concerns the development and testing of a new approach for using technical mapping standards in the quality assessment of buildings automatically extracted from VHR satellite imagery. Feature extraction software was employed to map buildings present in a pansharpened QuickBird image of Lisbon. Quality assessment was exhaustive and involved comparisons of extracted features against a reference data set, introducing cartographic constraints from scales 1:1000, 1:5000, and 1:10,000. The spatial data quality elements subject to evaluation were: thematic (attribute) accuracy, completeness, and geometric quality assessed based on planimetric deviation from the reference map. Tests were developed and metrics analyzed considering thresholds and standards for the large mapping scales most frequently used by municipalities. Results show that values for completeness varied with mapping scales and were only slightly superior for scale 1:10,000. Concerning the geometric quality, a large percentage of extracted features met the strict topographic standards of planimetric deviation for scale 1:10,000, while no buildings were compliant with the specification for scale 1:1000.     

利用机载LiDAR数据和高分辨率图像提取复杂城区建筑物

在复杂城区内部通常存在大量的阴影,建筑物的屋顶也有多种类型,这使得利用高分辨率遥感图像自动提取建筑物变得困难。针对上述2个问题,提出了一种综合利用高分辨率图像与机载Li DAR数据的城市建筑物提取新方法。首先,对归一化植被指数(normalized difference vegetation index,NDVI)和Li DAR高度数据设定阈值得到初步的建筑物提取结果;然后,分别利用阴影区NDVI、图像纹理和形态学滤波来改进结果;最后,采用局部的机载Li DAR数据和Quick Bird图像,对提出的方法进行验证,并与现有方法进行比较。研究结果表明,该方法可有效减少由阴影和不同屋顶特征所造成的错误识别,显著提高了建筑物提取精度。     

Parameter evaluation and optimization for multi-resolution segmentation in object-based shadow detection using very high resolution imagery

Object-based shadow detection in urban areas is an important topic in very high resolution remote sensing image processing. Multi-resolution segmentation (MRS) is an effective segmentation method, and is used for object-based shadow detection. However, several input parameters within MRS may result in unstable performance for final shadow detection; thus, the evaluation and optimization for the parameters upon the final shadow detection accuracy cannot be overlooked. In this paper, the three parameters in MRS (scale s, weight of colour w_color and weight of compactness w_compact) upon the final result of a recently proposed method, object-based shadow detection with Dempster–Shafer theory, were evaluated and optimized by sensitivity analysis and Taguchi’s method with three experimental data. Experiments show that scale s is the most sensitive parameter among the three parameters within MRS. More importantly, according to the Taguchi’s method theory, there is a very significant interaction effect between s and w_color, which cannot be overlooked. The shadow detection accuracy yielded by the optimum parameter combination in consideration of the interaction effect is higher than that only optimized by covering the main effect of single parameter in most cases.     

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.     

Exploiting spectral and spatial information in hyperspectral urban data with high resolution

Very high resolution hyperspectral data should be very useful to provide detailed maps of urban land cover. In order to provide such maps, both accurate and precise classification tools need, however, to be developed. In this letter, new methods for classification of hyperspectral remote sensing data are investigated, with the primary focus on multiple classifications and spatial analysis to improve mapping accuracy in urban areas. In particular, we compare spatial reclassification and mathematical morphology approaches. We show results for classification of DAIS data over the town of Pavia, in northern Italy. Classification maps of two test areas are given, and the overall and individual class accuracies are analyzed with respect to the parameters of the proposed classification procedures.     

厦门典型居住区环境质量高分辨率遥感评价

本文根据高分辨率遥感影像的特点,以及给定的人居环境区即居住单元定义、以及划分方法得到了厦门市四个典型的城市居住单元各地物种类分布图等研究数据。依据上述数据,在选取建筑容积率参数基础上,同时研究并提出了平均可视面积等指标及其相关分析方法。通过对这些参数分别进行计算,最终得到各相关参数的具体结果。最后采用模糊评判的方法,并通过问卷调查和专家打分等方式得到各参数指标的评判矩阵,通过运算得到各居住单元综合指标评价的最终结果。