Urban Area Detection Research Articles

Congestion Detection and Propagation in Urban Areas Using Histogram Models

Rapid growth of urbanization makes the roadways exacerbate many problems like traffic congestion, road accidents, and passenger discomfort. Many actions have been taken globally to solve or reduce this impact but still the congestion problem seems to be persistent globally. In this paper, we propose a new histogram-based model for congestion detection. We subsequently extended our model with the base platform concept and use Intelligent Transportation System (ITS) technologies to provide a novel rerouting strategy. The proposed model enables the microscopic visualization of the traffic patterns for every individual lane and predicts the congestion in priori and takes actions proactively. The rerouting strategy used in our approach provides a novel solution to the congestion problem by taking precaution measures prior to the critical point of congestion occurrence. The proposed algorithm is compared with various existing algorithms and the simulation results show that the proposed model addresses the congestion problem effectively and provides better solution compared to existing algorithms.

Water Detection in Urban Areas from GF-3.

The rapid and accurate detection of urban water is critical for urban management, river detection, and flood disaster assessment. This study is devoted to detecting water by GaoFen-3 (GF-3) Synthetic Aperture Radar (SAR) images with high spatial resolution. There have been no effective solutions that discriminate water and building shadows using a single SAR image in previous research. Inspired by the principle that every shadow has a corresponding building nearby, a new method is proposed in this study, whereby building shadows are removed depending on the correspondence of buildings and their shadows. The proposed method is demonstrated effective and efficient by experimental results on six GF-3 SAR images. The Receiver Operating Characteristic (ROC) curves of the water detection results indicate that the proposed method increases the Probability of Detection (PD) to 98.36% and decreases the Probability of False Alarm (PFA) to 1.91% compared with the thresholding method, where, at the same PFA level, the maximum PD of the thresholding method is 72.62% in all testing samples. The proposed method is capable of removing building shadows and detecting water with high precision in urban areas, which presents the great potential of high-spatial-resolution GF-3 images in terms of water resource management.

Urban Area Detection in Very High Resolution Remote Sensing Images Using Deep Convolutional Neural Networks

Detecting urban areas from very high resolution (VHR) remote sensing images plays an important role in the field of Earth observation. The recently-developed deep convolutional neural networks (DCNNs), which can extract rich features from training data automatically, have achieved outstanding performance on many image classification databases. Motivated by this fact, we propose a new urban area detection method based on DCNNs in this paper. The proposed method mainly includes three steps: (i) a visual dictionary is obtained based on the deep features extracted by pre-trained DCNNs; (ii) urban words are learned from labeled images; (iii) the urban regions are detected in a new image based on the nearest dictionary word criterion. The qualitative and quantitative experiments on different datasets demonstrate that the proposed method can obtain a remarkable overall accuracy (OA) and kappa coefficient. Moreover, it can also strike a good balance between the true positive rate (TPR) and false positive rate (FPR).

In brief

PAGE 395 A solution to avoid relay attacks on keyless entry systems in vehicles is proposed in work from Korea. In conventional systems, the proximity of the key to the vehicle is approximated using received signal strength. The proposed approach applies an additional layer of security using a channel correlation-based proximity authentication scheme to prevent relay attacks. PAGE 376 Researchers in the USA report the use of a digital filter with adaptive coefficients to correct current sensing and temperature variation in DC/DC switch mode power supplies. Tighter output voltage regulation is achieved with this filter and current sensing can be applied to systems without adaptive voltage positioning. The filter is suitable for computing, telecom, and storage applications. A channel correlation-based proximity authentication scheme for keyless entry systems for vehicles. PAGE 385 Digital television signals in urban environments are used to improve passive radar target detection performance in work from China. This work combines traditional polarisation theories with passive radar detection to reduce interference within built up urban environments using a dual-polarisation antenna array. Correct current sensing with a digital filter with adaptive coefficients for DC/DC switch mode power supplies. PAGE 336 Researchers in Japan make a combined wireless local area network system and smart antenna to suppress interference in busy locations, such as shopping malls and stadiums. The device has eight internal antennas that are dynamically selected by high-speed switching to maintain network service. A large improvement in interface suppression with this device is demonstrated in comparison with centralised antennas. Passive target detection in urban areas is improved by taking advantage of polarisation diversity in digital television signals. PAGE 359 Electromagnetic reflectivity from twill-weave carbon fibre reinforced polymers (CFRP) is measured in work from Austria. Different fibre patterns of the CFRP were investigated, including unidirectional, shredded and twill-weave fibres. Twill-weave fibres showed a sharp angular dependency in reflectivity in response to millimetre waves at 60 GHz. The fibres are manufacturing materials for vehicles and are targets for millimetre wave radar. Integration of eight internal antennas into a wireless local area network system to suppress interference in busy locations. The electromagnetic reflectivities of different patterns of carbon fibre reinforced polymers are measured.

Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning

To improve the accuracy of change detection in urban areas using bi-temporal high-resolution remote sensing images, a novel object-based change detection scheme combining multiple features and ensemble learning is proposed in this paper. Image segmentation is conducted to determine the objects in bi-temporal images separately. Subsequently, three kinds of object features, i.e., spectral, shape and texture, are extracted. Using the image differencing process, a difference image is generated and used as the input for nonlinear supervised classifiers, including k-nearest neighbor, support vector machine, extreme learning machine and random forest. Finally, the results of multiple classifiers are integrated using an ensemble rule called weighted voting to generate the final change detection result. Experimental results of two pairs of real high-resolution remote sensing datasets demonstrate that the proposed approach outperforms the traditional methods in terms of overall accuracy and generates change detection maps with a higher number of homogeneous regions in urban areas. Moreover, the influences of segmentation scale and the feature selection strategy on the change detection performance are also analyzed and discussed.

Blow flies as urban wildlife sensors

Wildlife detection in urban areas is very challenging. Conventional monitoring techniques such as direct observation are faced with the limitation that urban wildlife is extremely elusive. It was recently shown that invertebrate-derived DNA (iDNA) can be used to assess wildlife diversity in tropical rainforests. Flies, which are ubiquitous and very abundant in most cities, may also be used to detect wildlife in urban areas. In urban ecosystems, however, overwhelming quantities of domestic mammal DNA could completely mask the presence of wild mammal DNA. To test whether urban wild mammals can be detected using fly iDNA, we performed DNA metabarcoding of pools of flies captured in Berlin, Germany, using three combinations of blocking primers. Our results show that domestic animal sequences are, as expected, very dominant in urban environments. Nevertheless, wild mammal sequences can often be retrieved, although they usually only represent a minor fraction of the sequence reads. Fly iDNA metabarcoding is therefore a viable approach for quick scans of urban wildlife diversity. Interestingly, our study also shows that blocking primers can interact with each other in ways that affect the outcome of metabarcoding. We conclude that the use of complex combinations of blocking primers, although potentially powerful, should be carefully planned when designing experiments.

Integrated Change Detection and Classification in Urban Areas Based on Airborne Laser Scanning Point Clouds.

This paper suggests a new approach for change detection (CD) in 3D point clouds. It combines classification and CD in one step using machine learning. The point cloud data of both epochs are merged for computing features of four types: features describing the point distribution, a feature relating to relative terrain elevation, features specific for the multi-target capability of laser scanning, and features combining the point clouds of both epochs to identify the change. All these features are merged in the points and then training samples are acquired to create the model for supervised classification, which is then applied to the whole study area. The final results reach an overall accuracy of over 90% for both epochs of eight classes: lost tree, new tree, lost building, new building, changed ground, unchanged building, unchanged tree, and unchanged ground.

Detection of Urban Areas using Genetic Algorithms and Kohonen Maps on Multispectral images

In this article, the detection of urban areas on satellite multispectral Landsat images. The goal is to improve the visual interpretations of images from remote sensing experts who often remain subjective. Interpretations depend deeply on the quality of segmentation which itself depends on the quality of samples. A remote sensing expert must actually prepare these samples. To enhance the segmentation process, this article proposes to use genetic algorithms to evolve the initial population of samples picked manually and get the most optimal samples. These samples will be used to train the Kohonen maps for further classification of a multispectral satellite image. Results are obtained by injecting genetic algorithms in sampling phase and this paper proves the effectiveness of the proposed approach.

PRE-PROCESSES FOR URBAN AREAS DETECTION IN SAR IMAGES

Abstract. In this study, pre-processes for urban areas detection in synthetic aperture radar (SAR) images are examined. These pre-processes are image smoothing, thresholding and white coloured regions determination. Image smoothing is carried out to remove noises then thresholding is applied to obtain binary image. Finally, candidate urban areas are detected by using white coloured regions determination. All pre-processes are applied by utilizing the developed software. Two different SAR images which are acquired by TerraSAR-X are used in experimental study. Obtained results are shown visually.

USING COVARIANCE MATRIX FOR CHANGE DETECTION OF POLARIMETRIC SAR DATA

Abstract. Nowadays change detection is an important role in civil and military fields. The Synthetic Aperture Radar (SAR) images due to its independent of atmospheric conditions and cloud cover, have attracted much attention in the change detection applications. When the SAR data are used, one of the appropriate ways to display the backscattered signal is using covariance matrix that follows the Wishart distribution. Based on this distribution a statistical test for equality of two complex variance-covariance matrices can be used. In this study, two full polarization data in band L from UAVSAR are used for change detection in agricultural fields and urban areas in the region of United States which the first image belong to 2014 and the second one is from 2017. To investigate the effect of polarization on the rate of change, full polarization data and dual polarization data were used and the results were compared. According to the results, full polarization shows more changes than dual polarization.

Sensitivity of Sentinel-1 backscatter to characteristics of buildings

ABSTRACTIn this study, Sentinel-1 interferometric wide swath (IW) mode backscatter is analysed with respect to physical parameters of buildings in Tallinn, Estonia. Dependence on height, alignment, density, shape, and material is shown and discussed. Distribution of backscatter was estimated with respect to each of the parameters, and a correlation matrix of all physical parameters and backscatter values was computed. Height has the strongest effect on backscatter values for both polarization bands, while shape and alignment to orbit has weaker effect on the backscatter. Relationship of co-polarized and cross-polarized backscatter with how a building is aligned with respect to the satellite’s look angle indicates that double bounce from wall–ground interactions is still the dominant scattering mechanism detected by Sentinel-1 in IW mode with 20 m resolution. In order to establish possible detection problems related to specifically oriented buildings at different latitudes, dihedral backscatter is modelled for buildings of oblong and square shapes. Results from this study should be used to improve existing and develop new urban area detection methods based on Sentinel-1 data.

Automatic detection of urban area from the remote sensing imagery based on improved D-S evidence theory

ABSTRACTUrban area detection from the remote sensing imagery has important significance in dynamic monitoring on the use types of city land and the city development planning. An approach for full-automatic detection of urban area is proposed, which can fuse any number of features. The combination formula of the traditional D-S evidence theory (TDSET) is improved by adding a processing factor for conflicting evidences based on the original fusion function. For the validity of the proposed method, three positive evidences (gradient mean, Harris feature points and spectral homogeneity) as well as one negative evidence (local gradient orientation density) are selected to test three GeoEye images. Experimental results show that the precision of the proposed method achieves 96.89% and its recall is 86.52%, where the recall increases about 50% comparing with the TDSET.

Urban Change Analysis with Multi-Sensor Multispectral Imagery

An object-based method is proposed in this paper for change detection in urban areas with multi-sensor multispectral (MS) images. The co-registered bi-temporal images are resampled to match each other. By mapping the segmentation of one image to the other, a change map is generated by characterizing the change probability of image objects based on the proposed change feature analysis. The map is then used to separate the changes from unchanged areas by two threshold selection methods and k-means clustering (k = 2). In order to consider the multi-scale characteristics of ground objects, multi-scale fusion is implemented. The experimental results obtained with QuickBird and IKONOS images show the superiority of the proposed method in detecting urban changes in multi-sensor MS images.

Building Detection in Urban Areas using Features Fusion of Optical and Radar Images in Neural Networks

Building Detection in Urban Areas using Features Fusion of Optical and Radar Images in Neural Networks

Object-Based Change Detection in Urban Areas: The Effects of Segmentation Strategy, Scale, and Feature Space on Unsupervised Methods

Object-based change detection (OBCD) has recently been receiving increasing attention as a result of rapid improvements in the resolution of remote sensing data. However, some OBCD issues relating to the segmentation of high-resolution images remain to be explored. For example, segmentation units derived using different segmentation strategies, segmentation scales, feature space, and change detection methods have rarely been assessed. In this study, we have tested four common unsupervised change detection methods using different segmentation strategies and a series of segmentation scale parameters on two WorldView-2 images of urban areas. We have also evaluated the effect of adding extra textural and Normalized Difference Vegetation Index (NDVI) information instead of using only spectral information. Our results indicated that change detection methods performed better at a medium scale than at a fine scale where close to the pixel size. Multivariate Alteration Detection (MAD) always outperformed the other methods tested, at the same confidence level. The overall accuracy appeared to benefit from using a two-date segmentation strategy rather than single-date segmentation. Adding textural and NDVI information appeared to reduce detection accuracy, but the magnitude of this reduction was not consistent across the different unsupervised methods and segmentation strategies. We conclude that a two-date segmentation strategy is useful for change detection in high-resolution imagery, but that the optimization of thresholds is critical for unsupervised change detection methods. Advanced methods need be explored that can take advantage of additional textural or other parameters.

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 wcolor and weight of compactness wcompact) 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 wcolor, 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.

Detecting the Boundaries of Urban Areas in India: A Dataset for Pixel-Based Image Classification in Google Earth Engine

Urbanization often occurs in an unplanned and uneven manner, resulting in profound changes in patterns of land cover and land use. Understanding these changes is fundamental for devising environmentally responsible approaches to economic development in the rapidly urbanizing countries of the emerging world. One indicator of urbanization is built-up land cover that can be detected and quantified at scale using satellite imagery and cloud-based computational platforms. This process requires reliable and comprehensive ground-truth data for supervised classification and for validation of classification products. We present a new dataset for India, consisting of 21,030 polygons from across the country that were manually classified as “built-up” or “not built-up,” which we use for supervised image classification and detection of urban areas. As a large and geographically diverse country that has been undergoing an urban transition, India represents an ideal context to develop and test approaches for the detection of features related to urbanization. We perform the analysis in Google Earth Engine (GEE) using three types of classifiers, based on imagery from Landsat 7 and Landsat 8 as inputs. The methodology produces high-quality maps of built-up areas across space and time. Although the dataset can facilitate supervised image classification in any platform, we highlight its potential use in GEE for temporal large-scale analysis of the urbanization process. Our methodology can easily be applied to other countries and regions.

МОДЕЛЬ ФОНА ПРИ ДЕТЕКТИРОВАНИИ ДЫМА ПО ВИДЕОПОСЛЕДОВАТЕЛЬНОСТЯМ НА ОТКРЫТЫХ ПРОСТРАНСТВАХ

Introduction: Early smoke detection on videos obtained from outdoor video cameras is very important because smoke usually becomes visible earlier than flame and can be considered an objective evidence of a fire. Optical surveillance systems are cheap and therefore commonly used for smoke detection both in urban areas and in terrestrial forests where special fire watchtowers are maintained. Smoke is detected when objects on the video have specific motion, color, texture and shape. The resulting quality of the smoke detection algorithm depends on the frame resolution and on the distance to the objects being shot. Purpose: Background models should be build for the close and remote scenes taking into account the atmospheric and meteorological conditions. Results: We have studied two approaches to determining the scene depth: using the dark channel and a Markov random field, in both cases taking into account the law of direct attenuation of light waves in open spaces and the influence of atmospheric light. The method based on a Markov random field provided better results as compared to the method of the dark channel, as in the latter the pixel intensity is analyzed without considering the environment. Practical relevance: The found scene depth allows us to split the images into two groups: close scenes (up to about 500 m) and remote scenes (more than 500 m), where “close” and “far” smoke can be watched, respectively. During the experiments, we analyzed 100 images with close scenes and 100 images with remote scenes. The proposed method is efficient because different sets of smoke features are used for close and remote scenes.

Extraction of Urban Areas From Polarimetric SAR Imagery

Polarimetric synthetic aperture radar (PolSAR) images are extensively used for land-use/land-cover (LULC) classification. One of the important issues in radar remote sensing is urban area detection, where difficulties are found because of its heterogeneity. In this paper, we are interested in urban area detection using PolSAR images which allow us detecting the scattering mechanisms by the use of polarimetric target decompositions methods. We propose in this paper two methods: in the first one, we use the powers of Yamaguchi four-component decomposition and in the second method, we use the coefficients of PolSAR covariance matrix calculated in the circular polarization basis. We added in each method the complex Wishart maximum likelihood (ML) classifier to refine the classification results. To validate both methods, we used two PolSAR images acquired in C-band by RADARSAT-2 satellite over the El Hamiz city in Algeria and San Francisco Bay. The two proposed algorithms give accurate results in both test sites, with superiority of the circular condition method.

A new method for road detection in urban areas using high-resolution satellite images and Lidar data based on fuzzy nearest-neighbor classification and optimal features

Detection of roads in urban areas is of greater importance and is a persistent research focus in the remote sensing community. The spectral and geometrical varieties of road pixels; their spectral similarity to other features such as buildings, parking lots, and sidewalks; and the occasional obstruction by vehicles and trees are obstacles to the precise identification of urban roads through satellite images. Lidar data, however, provide height information that can facilitate the identification of roads from other spectrally similar elements. Therefore, Lidar has been widely used alongside satellite images to identify features such as roads. In this paper, high-resolution QuickBird satellite imagery and Lidar data processed through nearest-neighbor classification based on optimal features have been used together to extract various types of urban roads. This work designed and implemented a rule-oriented strategy based on a masking approach. A supplementary strategy based on optimal design features was also used. The overall precision of class identification is 91 % with a kappa coefficient of 0.87, which shows a satisfactory precision according to different conditions and considerable interclass noise. The final results demonstrate the high capability of object-oriented methods in simultaneous identification of a wide variety of road elements in complex urban areas using both high-resolution satellite imagery and Lidar data.