Extraction of bridges over water from high-resolution optical remote-sensing images based on mathematical morphology

Bridges over water are typical man-made structures on the land’s surface. An accurate extraction of such bridges from high-resolution optical remote-sensing images plays an important role in civil, commercial, and military applications. Considering the complex features of ground objects within high-resolution optical remote-sensing images and the inefficiency of previous methods of bridge extraction with random bridge orientation, direction-augmented linear structuring elements were constructed and applied in this study by using mathematical morphology to identify and extract bridges over water with different orientations. First, the image pre-processing is performed to facilitate the object extraction. Then by using the histogram-based threshold segmentation method, water bodies such as rivers are extracted and described as a binary image. Based on water bodies, the appropriate direction-augmented linear structuring element is then selected. Together with mathematical morphology operations, such as dilation and erosion, potential bridges are extracted by overlay analysis. Assisted by prior knowledge of bridges, false bridges are screened out and post-processing is finally performed to refine the extracted true bridges. This approach was validated with experiments in Shanghai and Beijing, China. The results show that the direction-augmented linear structuring elements are of high precision and have the capability of extracting bridges over water in different directions within the high-resolution optical remote-sensing image, considering both qualitative and quantitative aspects. Therefore, this approach may be useful in updating geographical databases of bridges and facilitating the assessment of bridge damage caused by natural disasters.     

Texture analysis of IKONOS panchromatic data for Douglas-fir forest age class separability in British Columbia

This Letter presents the results of textural separability tests obtained by first- and second-order texture methods on different aged Douglas-fir stands in IKONOS panchromatic imagery acquired 3 June 2000 over the Sooke River watershed in British Columbia. The effects of different measures and window sizes on the textural separability are discussed. Small windows sizes were not as effective in separating stands as larger windows sizes. Second-order (spatial co-occurrence homogeneity) texture values were the most effective in distinguishing between age classes. A first-order (variance) texture measure, though still useful, provided less separability.     

Extraction of fluvial networks from SPOT panchromatic data in a low relief,arid basin

Abstract

Euclidean distance classification of SPOT panchromatic data was used to delineate ephemeral, fluvial networks on low-relief, alluvial fan surfaces in an arid basin in the southwestern United States. The SPOT classified channel network was registered to a Universal Transverse Mercator (UTM) projection and compared to areas of channelized flow and a Strahler-ordered channel network extracted from U.S. Geological Survey 7½ minute topographic maps and black-and-white aerial photographs. One-pixel and two-pixel proximity searches of the SPOT classified image correctly classified over 80 per cent of the areas of channelized flow, 77 per cent of the second order and greater channel network, and 98 per cent of the fourth order and greater channel network. The SPOT channel network was simplified by elimination of lower-order channel segments to produce a network closely resembling (in terms of number of channel segments and total channel length) a network created for hydrological modelling from topographic maps. This suggests that SPOT panchromatic data can be used to delineate fluvial network for hydrological investigations in similar physiographic settings without existing topographic map or aerial photographic coverage.     

Extraction of urban impervious surfaces from an IKONOS image

Impervious surface has been recognised as an important indicator in urban environmental assessment. However, accurate extraction of impervious surface information in urban areas is a challenge because of the complexity of impervious materials. This paper explores different approaches for impervious surface extraction with IKONOS imagery in Indianapolis, U.S.A., by using decision tree classifier (DTC) and linear spectral mixture analysis (LSMA). This research indicates that DTC is an effective approach for extraction of different impervious surface classes, including high‐, medium‐ and low‐reflectivity impervious surfaces and that LSMA‐based approach can provide quantitative measure of imperviousness. A critical step is to separate dark impervious objects/features from shadows cast by tall buildings and tree canopy and from water.     

Within-field wheat yield prediction from IKONOS data: a new matrix approach

This study demonstrates a unique matrix approach to determine within-field variability in wheat yields using fine spatial resolution 4?m IKONOS data. The matrix approach involves solving a system of simultaneous equations based on IKONOS data and post-harvest yields available at entire field scale. This approach was compared with a regression-based modelling approach involving field-sensor measured yields and the corresponding IKONOS measured indices and wavebands. The IKONOS data explained 74–78% variability in wheat yield. This is a significant result since the finer spatial resolution leads to capturing greater spatial variability and detail in landscape relative to coarser spatial resolution data. A pixel-by-pixel mapping of wheat yield variability highlights the fine spatial detail provided by IKONOS data for precision farming applications.     

IKONOS 1m城市图像中道路信息提取方法研究

从遥感图像中提取道路特征已有许多研究，但仍然存在许多问题有待解决。该文主要对IKONOS 1m分辨率遥感城市图像的半自动提取方法进行一般性的研究，并且对各个方法的特点进行了分析，可以针对不同的情况，适当的选择不同的方法，对城市中智能交通起到决策性的作用。     

Fusion of point-based postal data with IKONOS imagery

Research in urban remote sensing has been recently reinvigorated by both the continuing fusion with GIS and the advent of high spatial resolution satellite sensor data. Both will be examined by this paper in terms of how GIS data at the point level can assist the identification and interpretation of urban land use patterns from classified land cover. Specifically, how spatial statistics can be used to summarise the two-dimensional patterns of point data representing residential and commercial buildings. In this paper point data refer to the location of postal addresses known as ADDRESS-POINT^TM and collected by the Ordnance Survey of Great Britain and COMPAS^TM in Northern Ireland. Groups of these postal points are characterised using standard nearest-neighbour and linear nearest-neighbour indices in terms of the spacing and arrangement of residential and commercial buildings. The indices then form the basis for the interpretation of urban pixels classified from IKONOS imagery at the 4 m spatial resolution. In addition, the paper will outline an agenda for constructing an automated pattern recognition system that would ultimately identify and characterise the physical arrangement of buildings in terms of density (compactness versus sparseness) and linearity. Preliminary results so far are most encouraging. Using ground truth from aerial photographs at 15 cm spatial resolution, classified IKONOS imagery representing two cities in the United Kingdom, Bristol and Belfast, have been investigated. In both, spatial patterns have demonstrated the ability to identify misclassified urban pixels and characterise a variety of building arrangements. Also, using the software e-Cognition, a spatial classification based on nearest neighbour contextual rules produced accuracies of 95.4% compared to 90.7% from a multispectral-only classification. Further, more extensive testing is continuing.     

基于改进的渐进纹理分析技术提取IKONOS影像中的城市主要道路

提出了一种从IKONOS多光谱影像提取城市主要道路的方法。首先对影像进行3个层次的纹理分析。第一层为检测集像元与训练集像元在波段空间中的闵氏距离;第二层为检测集像元及其3×3窗口内像元分布与训练集像元在波段空间中的巴氏距离;第三层为检测集像元及其3×3窗口内像元分布与训练集像元在彩色纹理特征空间中的巴氏距离。对上述获取的结果分别进行了阈值分割、细化,并结合道路的几何特征,采用模糊数学的方法对各个图层进行了融合。接着提出了一种基于道路知识的道路段连接算法。最后用多项式拟合方法对连接结果进行了优化,获得了较好的提取结果。     

Shadow detection and building-height estimation using IKONOS data

The spectral confusion between shadow and water (or other dark surfaces) often results in suboptimal urban classification performances, especially from high-resolution satellite imagery (e.g. IKONOS). A classification method was developed to incorporate spatial indices of image objects to improve the shadow/water detection. A number of spatial indices, such as size, shape and spatial neighbour of image objects, were characterized to differentiate water and shadow objects. This generated superior shadow/water detection performance compared to a traditional per-field Extraction and Classification of Homogeneous Objects (ECHO) classification method. The user's accuracies for shadow and water classes were increased to 88% and 92%, compared to 80% and 76% obtained from the traditional ECHO classification approach. Furthermore, an automated approach was developed for shadow-length and corresponding building-height estimation. The accuracy assessment suggested good results for very high buildings, especially for isolated high-rise buildings.     

Object-based fusion for urban tree species classification from hyperspectral,panchromatic and nDSM data

This study aims at identifying the best object-based fusion strategy that takes advantage of the complementarity of several heterogeneous airborne data sources for improving the classification of 15 tree species in an urban area (Toulouse, France). The airborne data sources are: hyperspectral Visible Near-Infrared (160 spectral bands, spatial resolution of 0.4 m) and Short-Wavelength Infrared (256 spectral bands, 1.6 m), panchromatic (14 cm), and a normalized Digital Surface Model (12.5 cm). Object-based feature and decision level fusion strategies are proposed and compared when applied to a reference site where the species are previously identified during ground truth collection. This allows the best fusion strategy to be selected with a view to introducing the method in an automatic process (tree crown delineation and species classification) on a test site, independent of the reference site used for learning. In particular, a decision level fusion is selected: based on the Support Vector Machine algorithm, Visible Near-Infrared and Short-Wavelength Infrared classifications use Minimum Noise Fraction components at the original spatial resolution, whereas panchromatic and normalized Digital Surface Model classifications use, respectively, Haralick’s and structural features computed at the object scale. After the computation of a decision profile for each source at the object level based on the classification algorithms’ membership probabilities, these decision profiles are combined and a decision rule is applied to predict the species. Focusing on the reference site, the Visible Near-Infrared exhibits the best performances with F-score values higher than 60% for 13 species out of 15. The Short-Wavelength Infrared is the most powerful for three species with F-score greater than 60% for seven common species with the Visible Near-Infrared. The panchromatic and normalized Digital Surface Model contribute marginally. The best fusion strategy (decision fusion) does not improve significantly the overall accuracy with 77% (kappa = 74%) against 75% (kappa = 72%) for the Visible Near-Infrared but in general, it improves the results for cases where complementarities have been observed. When applied to the test site and assessed for the two majority species (Tilia tomentosa and Platanus x hispanica), the selected approach gives consistent results with an overall accuracy of 63% against 55% for the Visible Near-Infrared.     

Extraction of data from preprinted forms

The widespread use of printed forms for data acquisition makes the ability to automatically read and analyze their contents desirable. The components of a forms analysis system include conversion from paper to an image through scanning, image enhancement, document identification, data extraction, and data interpretation. This paper describes techniques for manipulating electronic images of forms in preparation for data interpretation. A combination feature extraction/model-based approach is used for forms identification, registration, and field extraction. Forms identification is implemented with a neural network. The system is demonstrated on United States Internal Revenue Service forms.     

基于缨帽变换的IKONOS数据融合

应用缨帽变换（KT）和主成分分析（PCA）方法,对北京圆明园周围的IKONOS全色波段和多光谱波段进行融合,并分别对整景影像和植被区的融合结果进行对比研究,结果表明：在该实验区,整景影像和植被区缨帽变换融合结果都优于主成分析结果,不同的缨帽变换系数矩阵在整景影像和植被区融合质量会有差别,应根据具体地点、时相,分类方法做适当的选择。     

基于影像融合的IKONOS影像阴影信息自动提取方法

高分辨率卫星影像中的地物阴影是其特有的组成部分，如何有效地提取和利用这些阴影信息对于高分辨率卫星影像的应用是一个具有重要意义和实际价值的问题。本文提出在对IKONOS全色波段与多光谱的2、3、4波段进行基于IHS色度空间的影像融合的基础上，运用波谱角度映射表分类方法，对IKONOS影像中的阴影进行自动提取。试验结果表明该方法提取效果良好，平均精度可达85．3％。该方法为高分辨率卫星影像的阴影信息自动提取提供了一种有效途径。     

A wavelet transform method to merge Landsat TM and SPOT panchromatic data

To take advantage of the high spectral resolution of Landsat TM images and the high spatial resolution of SPOT panchromatic images (SPOT PAN), we present a wavelet transform method to merge the two data types. In a pyramidal fashion, each TM reflective band or SPOT PAN image was decomposed into an orthogonal wavelet representation at a given coarser resolution, which consisted of a low frequency approximation image and a set of high frequency, spatially-oriented detail images. Band-by-band, the merged images were derived by performing an inverse wavelet transform using the approximation image from each TM band and detail images from SPOT PAN. The spectral and spatial features of the merged results of the wavelet methods were compared quantitatively with those of intensity-hue-saturation (IHS), principal component analysis (PCA), and the Brovey transform. It was found that multisensor data merging is a trade-off between the spectral information from a low spatial-high spectral resolution sensor and the spatial structure from a high spatial-low spectral resolution sensor. With the wavelet merging method, it is easy to control this trade-off. Experiments showed that the simultaneous best spectral and spatial quality can only be achieved with wavelet transform methods, compared with the three other approaches examined.     

Segment-constrained regression tree estimation of forest stand height from very high spatial resolution panchromatic imagery over a boreal environment

Mean stand height is an important parameter for forest volume and biomass estimation in support of monitoring and management activities. Information on mean stand height is typically obtained through the manual interpretation of aerial photography, often supplemented by the collection of field calibration data. In remote areas where forest management practices may not be spatially exhaustive or where it is difficult to acquire aerial photography, alternate approaches for estimating stand height are required. One approach is to use very high spatial resolution (VHSR) satellite imagery (pixels sided less than 1 m) as a surrogate for air photos. In this research we demonstrate an approach for modelling mean stand height at four sites in the Yukon Territory, Canada, from QuickBird panchromatic imagery. An object-based approach was used to generate homogenous segments from the imagery (analogous to manually delineated forest stands) and an algorithm was used to automatically delineate individual tree crowns within the segments. A regression tree was used to predict mean stand height from stand-level metrics generated from the image grey-levels and within-stand objects relating individual tree crown characteristics. Heights were manually interpreted from the QuickBird imagery and divided into separate sets of calibration and validation data. The effects of calibration data set size and the input metrics used on the regression tree results were also assessed. The approach resulted in a model with a significant R² of 0.53 and an RMSE of 2.84 m. In addition, 84.6% of the stand height estimates were within the acceptable error for photo interpreted heights, as specified by the forest inventory standards of British Columbia. Furthermore, residual errors from the model were smallest for the stands that had larger mean heights (i.e., > 20 m), which aids in reducing error in subsequent estimates of biomass or volume (since stands with larger trees contribute more to overall estimates of volume or biomass). Estimated and manually interpreted heights were reclassified into 5-metre height classes (a schema frequently used for forest analysis and modelling applications) and compared; classes corresponded in 54% of stands assessed, and all stands had an estimated height class that was within ± 1 class of their actual class. This study demonstrates the capacity of VHSR panchromatic imagery (in this case QuickBird) for generating useful estimates of mean stand heights in unmonitored, remote, or inaccessible forest areas.     

Extraction of power-transmission lines from vehicle-borne lidar data

This paper presents a step-wise method for extracting power-transmission lines and towers from vehicle-borne light detection and ranging (lidar) data. First, this method estimates road ranges with regard to incidence angles and separates off-road points from road-surface points by applying elevation-difference and slope criteria to the road ranges scan-line by scan-line. Then, three filters, in terms of height, spatial density, and a combination of size and shape, are proposed to extract power-transmission line/power tower points from the identified off-road points, followed by the extraction of individual power-transmission lines via Hough transform and Euclidean distance clustering. Finally, a three-dimensional (3D) power-transmission line is modelled as a horizontal line in the x–y plane and a vertical catenary curve defined by a hyperbolic cosine function in the x–z plane. We evaluated the method using two data sets acquired by the RIEGL VMX-450 system. The average completeness, correctness, and quality of the extracted power-lines on two data sets are 0.92, 0.99, and 0.91, respectively, and the positional accuracies including root mean square error and maximum error averaged 0.07 and 0.05 m, respectively. The results show that the proposed method extracts power-transmission lines from large-scale, vehicle-borne lidar data with good thematic and positional accuracy.     

Evaluation of MODIS water vapour products over China using radiosonde data

Radiosonde data collected from 83 stations in China from January to December 2012 were used to evaluate Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared (NIR) and thermal infrared (IR) total precipitable water vapour (PWV) products. The results indicate that MODIS NIR PWV products shows better agreement with radiosonde data than with IR PWV products, with the correlation coefficients up to 0.95. The root mean square errors (RMSEs) of NIR PWV range from 2 to 8 mm with different stations, which shows significant regional differences over China. The mean RMSE is about 5.03 mm (~35%) with a positive deviation of 2.56 mm (~18%), indicating the occurrence of a slight overestimation. Moreover, MODIS IR PWV during night-time has a better agreement with radiosonde PWV than that during daytime. The mean RMSE of IR PWV during daytime was ~6.02 mm (~42%), with a positive deviation of 1.54 mm (~11%). The mean RMSE of IR PWV during night-time was ~5.81 mm (~40%), with a negative deviation of approximately ?0.04 mm (~0.25%). Both the NIR and IR PWV products during daytime tend to be higher than radiosonde PWV.     

A new approach for retrieving precipitable water from ATSR2 split-window channel data over land area

This paper presents a new algorithm to determine quantitatively column water vapour content ( W) directly from ATSR2 (Along-Track Scanner Radiometer) Split-Window radiance measurements. First, the Split-Window Covariance-Variance Ratio (SWCVR) method is reviewed. The assumptions made to derive this method are highlighted and its applicability is discussed. Then, an operational use of this method is developed and applied to several ATSR2 datasets. The water vapour contents retrieved using ATSR2 data from SGP'97 (USA), Barrax (Spain) and Cabauw (The Netherlands) are in good agreement with those measured by the quasi-simultaneous radiosonde. The mean and the standard deviation of their difference are 0.04 g cm^?2 and 0.22 g cm^?2, respectively. It is shown that water vapour content derived from ATSR2 data using the proposed algorithm is accurate enough in most cases for surface temperature determination with a split-window technique using ATSR2 data and for atmospheric corrections in visible and near-infrared channels of ATSR2.     

基于高分辨率卫星IKONOS影像的城市道路信息提取研究

与以往中、低分辨率卫星遥感信息提取相比，高分辨率卫星遥感信息提取有一定的特色，利用MATLAB软件对图像增强和区域增长形状提取方法，进行了城市道路信息提取，取得了较好的精度效果。     

Angular and polarization response of vegetation,bare soil and water from ADEOS POLDER data over India

In this letter, the angular variations of total and polarized reflectances of vegetation, bare soil and water with respect to different Sun-target-view angles, obtained from ADEOS (Advanced Earth Observation Satellite) POLDER (Polarisation and Directionality of Earth Reflectances) measurements acquired over the Indian sub-continent during 7 November 1996, are analysed at three wavelengths, namely, 443, 670 and 865 nm. The POLDER measured reflectances are approximately corrected for molecular and aerosol effects due to path radiance assuming a single layer, horizontally homogeneous atmosphere. The variation of total and polarized reflectances of three targets as a function of view zenith and relative azimuth angles are presented. The corrected target polarized reflectances show a monotonic decrease with relative azimuth and view zenith angles. A semi-empirical model was fitted to the corrected total reflectances for the three targets at three wavelengths. Statistical analysis of regression coefficients show that the coefficient corresponding to normalized reflectance, k₀, is physically significant.