Deriving fine-scale socioeconomic information of urban areas using very high-resolution satellite imagery

This article presents a new approach to derive fine-scale socioeconomic information of urban areas using very high resolution satellite data. The rationale behind the method is to use high resolution satellite data, capable of resolving urban morphology details, to derive a classification of the image. Thus, it is assumed that there is a relationship between the socioeconomic profile and the urban morphology of an area in terms of availability of green areas, sport facilities, private swimming pools or pavement conditions. The method is tested using a case study of Lima, Peru. Using a sample of ground data, a neural network classifier was applied to a pre-classified image in which entropy had been used to mask extensive, non-built up areas that would otherwise have inserted spurious information into the classifier. The result shows a high correlation (0.70 R ²) when compared with validation data. The good performances also show that a physiographic satellite view of the city reflects the socioeconomic layout of their inhabitants, thus making remote sensing a complementary tool for social research and urban planning. While the parameterization of the problem may differ from one area to another, it is shown that an a priori choice of a few parameters may help to automatically characterize large areas in social terms, thus allowing social inequality and its evolution to be mapped in those areas with limited availability of data. In order to make the method widely applicable, the possibilities and limitations of applying the procedure to other large cities are discussed.     

Interpretation of irrigation methods using airborne thermal infrared imagery

In areas of limited water resources for agriculture, management decisions could be enhanced by the availability of additional data about the amount and type of irrigation systems employed. Imagery produced using an HRB Singer AN/A AS 14 optical-electronic thermal infrared scanning system was found to be of great use for the interpretation of irrigation methods. Comparison of the interpreted irrigation method with ground information show agreement within 18 per cent.     

De-shadowing of airborne imagery using at-sensor downwelling irradiance data

Cast shadows caused by sparse clouds usually degrade spaceborne and airborne imagery. They result from the decrease of the direct solar beam due to the presence of a non-transparent cloud. The reduction of the downwelling solar flux density can be quantified during an air campaign, if the aircraft flies beneath the cloud and is equipped with an add-on instrument that measures the total downwelling solar irradiance. The objective of this work is to exploit such data for the de-shadowing of airborne hyperspectral imagery. Initially, the specific illumination and viewing conditions during the image acquisition, which allow the use of at-sensor downwelling irradiance data for the de-shadowing of airborne hyperspectral imagery, are outlined. Then a methodology is proposed that estimates the radiometric enhancement coefficients from the at-sensor irradiance data and correlates them with the image data using a shadow map. Improvements of the quality of the shadow maps are suggested. Performance assessment showed that at-sensor irradiance data could be satisfactorily utilized for compensating the cast shadow effects on remotely sensed imagery. It also highlighted the importance of generating and using an accurate shadow map and the particular difficulties for the air campaign planning raised by the requirement of exploitable at-sensor irradiance data.     

Automatic building extraction in dense urban areas through GeoEye multispectral imagery

In studies of high-resolution satellite (HRS) imagery, the extraction of man-made features such as roads and buildings has become quite attractive to the photogrammetric and remote-sensing communities. The extraction of 2D images from buildings in a dense urban area is an intricate problem, due to the variety of shapes, sizes, colours, and textures. To overcome the problem, many case studies have been conducted; however, they have frequently contained isolated buildings with low variations of shapes and colours and/or high contrast with respect to adjacent features. As an alternative, this study uses continuous building blocks along with high variation in shape, colour, radiance, size, and height. In addition, some non-building features include either the same or similar materials to that of building rooftops. Thus, there is low contrast between building and non-building features. The core components of the algorithm are: (1) multispectral binary filtering, (2) sub-clustering and single binary filtering, (3) multi-conditional region growing, and (4) post-processing. This approach was applied to a dense urban area in Tehran, Iran, and a semi-urban area in Hongshan district, Wuhan city, central China. A quantitative comparison was carried out between the proposed and three other algorithms for the Wuhan case study. GeoEye multispectral imagery was used in both case studies. The results show that the proposed algorithm correctly extracted the majority of building and non-building features in both case studies. The short running time of this algorithm along with precise manual editing can generate accurate building maps for practical applications.     

Lithological discrimination in central Snowdonia using airborne multispectral scanner imagery†

Abstract

An evaluation has been carried out of the use of 11-channel airborne multispectral scanner data as an input to large-scale geological mapping in upland Britain. The optimum index factor (OIF) provides a ranking of possible three-band combinations in terms of their uncorrelated spectral data content, while principal components analysis may be used to compress multi-channel data, thereby minimizing information loss. An assessment of the geological information content of various imagery products concluded that both high-value OIF and principal component images were useful for lithological discrimination of solid and superficial deposits whereas aerial photographs were better for identifying structural features.     

Shadow information recovery in urban areas from very high resolution satellite imagery

It is usually quite difficult to extract and recover shadow information in the urban environment from remote sensing imagery. This paper describes the study of precisely detected shadow in satellite images and recovering information from the surface covered in shadow from very high resolution (VHR) satellite imagery.     

Fusion of features in multi-temporal SAR imagery to detect changes in urban areas

The Dempster-Shafer (D-S) algorithm is improved to fuse different features of multi-temporal Synthetic Aperture Radar (SAR) images to detect changes in urban areas. Firstly, D-S theory is developed by not only considering the certainty of the sources, but also considering the average support of the sources to different subsets in the frame of discernment, in the process of evidence combination. In this way, it can assign conflict information in deferent sources reasonably and present a more reliable combination result. Secondly, amplitude ratio and distance between probability density distribution functions in Pearson graph of different temporal SAR images are extracted to present the change features in different scales. Lastly, the improved D-S algorithm is applied to fuse the two different features to detect change information of SAR images. An example of the Shanghai Lujiazui area using European Remote Sensing 2 (ERS-2) SAR images demonstrates the accuracy of the improved fusion algorithm.     

Feature selection and weighted SVM classifier-based ship detection in PolSAR imagery

Target decomposition is an important method for ship detection in polarimetric synthetic aperture radar (SAR) imagery. Parameters such as the polarization entropy and alpha angle deduced from the coherency matrix eigenvalue decomposition capture the differences between the target and background from different views separately. However, under the conditions of a relatively high resolution and a rough sea, the contrast between ship and sea reduces in the aforementioned space. Based on the analyses of target decomposition theory and the target’s scattering mechanism, multi-polarization parameters can be used to characterize different scattering behaviours of the ship target and sea clutter. Moreover, each parameter has its own diverse significance in the practical detection problem. This article proposes a feature selection and weighted support vector machine (FSWSVM) classifier-based algorithm to detect ships in polarimetric SAR (PolSAR) imagery. First, the method constructs a feature vector that consists of multi-polarization parameters. Then, different polarization parameters are refined and weighted according to their significance in the support vector machine (SVM) classifier. Finally, ships are classified from the sea background and other false alarms by the classifier. The validation results on National Aeronautics and Space Administration/Jet Propulsion Laboratory (NASA/JPL) airborne synthetic aperture radar (AIRSAR) and Radarsat-2 quad polarimetric data illustrate that the method detects ship targets more precisely and reduces false alarms effectively.     

An assessment of validation techniques for estimating chlorophyll-a concentration from airborne multispectral imagery

Accurate estimates of chlorophyll-a levels in coastal waters are required for the assessment of waters potentially subject to eutrophication. Traditional laboratory analysis of water samples does not offer the required spatial or temporal density of sampling. Remote sensing methods have been suggested as a more appropriate representation of the variability in chlorophyll a concentration encountered in UK coastal waters. This paper examines the use of two established techniques for the calibration of airborne multispectral imagery to determine chlorophyll-a concentration: the blue/green ratio and Fluorescence Line Height methods. These methods have been developed for use in oceanic or Canadian coastal waters. The errors incurred in the use of these techniques for calibration of data of UK coastal waters have not previously been addressed. This paper describes a rigorous assessment of the errors incurred, allowing the limitations of the techniques to be established. This has resulted in recommendations for chlorophyll-a measurement in the coastal zone.     

Unsupervised urban area extraction from SAR imagery using GMRF

This paper presents a new method for unsupervised urban area extraction from SAR imagery using two different GMRF models. One model is the T-based GMRF model proposed by Xavier Descombes specially for acquiring urban area in panchromatic SPOT imagery. When it is used for urban area extraction from SAR imagery, some missing detection occurs. The other model is the conventional GMRF model that requires training samples for urban area extraction. When it is used for SAR imagery, the extraction result includes all urban areas and some false detection. Three steps are made up in our method. First, we adopt a threshold for the T-based GMRF model parameter T to acquire the result of urban area extraction. Then, taking the result as training samples, we estimate the conventional GMRF model parameters and acquire a new result of urban area extraction. Finally, we fuse the two results above using a region-growing algorithm to form the final accurate urban area extraction. Experimental results show that the proposed unsupervised approach can obtain accurate urban area delineation. The text was submitted by the authors in English. Yang Yong. Born in 1978. Now a postgraduate in the Department of Communication and Information Systems, School of Electronic Information, Wuhan University. The research direction is Image Processing. Scientific interestsare SAR image segmentation and classification with the Markov random field approach. Hong Sun. Born in 1954. Graduated from the Huazhong University of Science and Technology of Electrical Engineering in 1982. Received a Doctoral degree in 1995. Author of Advanced Digital Signal Processing, which is widely used as a textbook for graduated students in China. Scientific interests include statistical signal processing, image analysis, and communication signal processing. Yongfeng Cao. Born in 1976. Graduated from Wuhan University of China in 1999. Assistant and doctoral candidate in the laboratory of Signal Processing and Modern Communication, School of Electronic Information, Wuhan University, China. Scientific interests include Markov random fields, Watershed transformation, and SAR image interpretation.     

Unsupervised urban area extraction from SAR imagery using GMRF

A new method is proposed to extract urban areas from SAR imagery using two different Gaussian Markov Random Field (GMRF) models. Firstly, by making an initial segmentation by a watershed algorithm, we adopt a particular GMRF model proposed by Descombes et al. (the model is called RGMRF model, distinguished from the conventional GMRF model) to acquire urban areas. In the first model a part of the urban areas from the SAR image is extracted with some missing detection. Then, taking the first result as a training sample, we use the conventional GMRF model to redo the extraction. In the second model a larger area is detected including all urban areas with some false detection. Finally, we fuse the two results using a region-growing algorithm to form the final detected urban area. Experimental results show that the proposed method can obtain accurate urban areas delineation. The text was submitted by the authors in English.     

An enhanced model for evacuation vulnerability assessment in urban areas

Rapid urbanization has led to a massive influx of people into cities. When many people congregate in urban areas, crowd crushing emergencies are likely to occur. If vulnerable areas with potential evacuation problems are detected in advance, crowd crushing emergencies may be minimized or even avoided. Thus, an evacuation vulnerability assessment from a precautionary perspective is fundamental. However, the current evacuation vulnerability assessment models are limited in spreading time estimation and evacuation capacity evaluation. To mitigate these limitations, in this study, we propose an enhanced model to quantitatively assess the evacuation vulnerability in urban areas. Our model enhances the current models in two ways. First, we employ a hexagon gridding scheme to construct a network to meet the prerequisite of evacuation spreading at the equal time intervals. Second, we quantify the grid connectivity on the network by considering the grid capacity to avoid underestimation of the evacuation vulnerability. Using a mobile phone location dataset of Shanghai, we systematically investigate the evacuation vulnerability of urban areas in a fine-grained spatio-temporal scale. Areas that may encounter evacuation problems to various degrees can be identified in advance. This information can support emergency management agencies in monitoring dense crowds and ensure early warnings of potential crowd crushing emergencies.     

Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: A comparison study

A significant proportion of high spatial resolution imagery in urban areas can be affected by shadows. Considerable research has been conducted to investigate shadow detection and removal in remotely sensed imagery. Few studies, however, have evaluated how applications of these shadow detection and restoration methods can help eliminate the shadow problem in land cover classification of high spatial resolution images in urban settings. This paper presents a comparison study of three methods for land cover classification of shaded areas from high spatial resolution imagery in an urban environment. Method 1 combines spectral information in shaded areas with spatial information for shadow classification. Method 2 applies a shadow restoration technique, the linear-correlation correction method to create a “shadow-free” image before the classification. Method 3 uses multisource data fusion to aid in classification of shadows. The results indicated that Method 3 achieved the best accuracy, with overall accuracy of 88%. It provides a significantly better means for shadow classification than the other two methods. The overall accuracy for Method 1 was 81.5%, slightly but not significantly higher than the 80.5% from Method 2. All of the three methods applied an object-based classification procedure, which was critical as it provides an effective way to address the problems of radiometric difference and spatial misregistration associated with multisource data fusion (Method 3), and to incorporate thematic spatial information (Method 1).     

The potential of kernel classification techniques for land use mapping in urban areas using 5m-spatial resolution IRS-1C imagery

Two techniques, integrating texture and spatial context properties for the classification of fine spatial resolution imagery from the city of Athens (Hellas) have been tested in terms of accuracy and class specificity. Both techniques were kernel based, using an artificial neural network and the kernel reclassification algorithm. The study demonstrated the high potential of the kernel classifiers to discriminate residential categories on 5 m-spatial resolution imagery. The overall accuracy percentages achieved were 73.44% and 74.22% respectively, considering a seven-class classification scheme. The adopted scheme was subset of the nomenclature referred to as 'Classification for Land Use Statistics Eurostat's Remote Sensing programme' (CLUSTERS) used by the Statistical Office of the European Communities (EUROSTAT) to map urban and rural environment.     

A Dempster Shafer-based fuzzy multisensor fusion system using airborne lidar and hyperspectral imagery

ABSTRACT

Nowadays, accurate spectral reflectance information is provided by hyperspectral (HS) data while light detection and ranging (lidar) data provides precise information about the height and geometrical properties of the surfaces. In the most research papers, data fusion of disparate sensors significantly improves object classification performance compared to that of just an individual sensor. Previous researches on fusion of these two sensors had problems such as crisp classifiers or simple fuzzy decision-making systems. This article tries to overcome these weaknesses by accurate support vector machine (SVM) and Fuzzy SVM as classifiers in crisp and fuzzy decision fusion system and fusion of two sensors by two different methods based on precise theories of Bayesian and Shafer. Also, the proposed method tries to compare the results of fusion of both data using decision fusion system with stacked features strategy. This study focuses on HS and lidar fusion through three main phases. The first phase is based on the using of Noise Weighted Harsanyi-Farrand-Chang method and principal component analysis to overcome the high dimensionality problem of HS data. The second phase is based on the feature extraction and selection strategy on lidar data. Finally, fuzzy SVM and Dempster Shafer methods are applied as fuzzy classification and fuzzy decision fusion strategies on the feature spaces. A co-registered HS and lidar data set from Houston of U.S.A. by 15 classes was available to examine the effectiveness of the proposed method. The results of this study highlight that the combination of HS and lidar data enable reliable mapping of land cover.     

Error assessment of validation techniques for estimating suspended particulate matter concentration from airborne multispectral imagery

Significant errors have been reported when transporting algorithms for converting aerial imagery to suspended particulate matter concentration to data collected at different times in different geographical sites. This raises the possibility that the relationships developed are coincidental rather than causal. Determination of the portability of the relationships will allow an assessment of the degree of causality of such algorithms. This paper describes the development of algorithms for two sites in UK coastal waters and the assessment of the errors incurred when porting these algorithms to datasets collected at different seasons and from varying geographical sites. The errors incurred when using algorithms at sites having differing morphologies are considered to be too large for operational use. The high accuracy of transfer to data from the same site at a different season indicates the causality of the relationship derived and allows the recommendation of a way forward for algorithm development.     

Geometric correction of airborne scanner imagery using orthophotos and triangulated feature point matching

Abstract

We present here a new scheme that performs geometrical correction for airborne multi-spectral scanner imagery provided that an adequate reference image, i.e., an orthophoto, is available. Instead of the ray-tracing method, we determine the corresponding ground coordinates for each pixel of an input image by integrating feature point matching under Delaunay triangulated networks. The major components of the scheme include progressive generation of registration control points, and image registration. Comprehensive investigation for quantitative error analysis is also included. Experimental results indicate that the corrected images attain an accuracy of better than 09 pixels.     

Extraction of earthquake-induced collapsed buildings using very high-resolution imagery and airborne lidar data

Extraction of urban building damage caused by earthquake disasters, from very-high-resolution (VHR) satellite imagery and related geospatial data, has been widely studied in the past decade. In this study, a multi-stage collapsed building detection method, using bi-temporal (pre- and post-earthquake) VHR images and post-earthquake airborne light detection and ranging (lidar) data, is proposed. Ground objects that are intact and significantly different from collapsed buildings, such as intact buildings, pavements, shadows, and vegetation, were first extracted using the post-event VHR image and lidar data and masked out. Collapsed buildings were then extracted by classifying the combined bi-temporal VHR images and texture images of the remaining area using a one-class classifier, the One-Class Support Vector Machine (OCSVM). A post-processing procedure was adopted to refine the obtained result. The proposed method was quantitatively evaluated and compared to two existing methods in Port au Prince, Haiti, which was heavily hit by an earthquake in January 2010. In the two comparative methods, data for the whole study area were directly used. In the first method, collapsed buildings were extracted directly using the OCSVM, while in the second method, buildings and pavements were removed from the extraction result of the first method. The results showed that the proposed method significantly outperformed the existing methods, with increases of 21% and 40%, respectively, in the kappa coefficient. The proposed method provides a fast and reliable method to detect collapsed urban buildings caused by earthquake disasters, and could also be applied to other study areas using similar data combinations.     

Building detection in an urban area using lidar data and QuickBird imagery

This article presents a hierarchical approach to detect buildings in an urban area through the combined usage of lidar data and QuickBird imagery. A normalized digital surface model (nDSM) was first generated on the basis of the difference between a digital surface model and the corresponding digital terrain model. Then, ground objects were removed according to a height threshold. In consideration of the relief displacement effect in very high resolution remote-sensing imagery, we segmented the nDSM by the region-growing method and used the overlap ratio to avoid over-removing building objects. Finally, the region size and spatial relation of trees and buildings were used to filter out trees occluded by buildings based on an object-based classification. Compared with previous methods directly using the normalized difference vegetation index (NDVI), our method improved the completeness from 85.94% to 90.20%. The overall accuracy of the buildings detected using the proposed method can be up to 94.31%, indicating the practical applicability of the method.     

Quantifying the main urban area expansion of Guangzhou using Landsat imagery

ABSTRACT

This article takes the main urban area of Guangzhou as the study area. Twelve land-use maps were interpreted from Landsat Thematic Mapper images using feature enhancement technology and Naïve Bayes-based weight vector AdaBoost (WV AdaBoost). The study shows that since 1987, with heavily cultivated land being lost and vegetation cover shrinking, the urban area has doubled in size. The effects of human activities on the landscape have become increasingly apparent and complicated. It also finds that the expansion pattern has been transformed from one in which the city’s main urban area engulfed the surrounding residential area to one in which surrounding towns expanded, driven by the development of the main urban area. By analysing the relationship between the social economy, population, and land-use change, we show that the forces driving expansion of the urban area have differed in different periods. The principal driving forces are national policymaking and adjustment, the aggregation and diffusion of population and economic development. Multitemporal land-use maps allow long-term dynamic monitoring of urban land cover in Guangzhou and provide important details of urban expansion and changes in land use. We employed intervals of 2 and 3 years, and our results provide good supporting data for studies of urban extension trends and reasonable urban planning.