Change Detection In Urban Areas Research Articles

A Trial of Urban Area Change Detection by Open Satellite Data Platform

A Trial of Urban Area Change Detection by Open Satellite Data Platform

A TomoSAR regularization-based method for height change detection in urban areas

Over the past years, urban change detection has always been a challenging issue for researchers. Due to the two-dimensional intrinsic property of satellite imageries and the inherent less intuitive interpretability of radar images, height change detection and SAR imagery-based change detection have been even more complicated tasks. However, TomoSAR-based height change detection is a novel topic on which only a few research works have ever been realized. This study explores the viability of tomographic-based change detection in urban areas, tackling the common challenges encountered in 2D change detection with SAR images. In contrast with the standard TomoSAR methods that usually suffer from the ill-posed inverse problem, the TomoSAR regularization-based methods are considered powerful aids for reconstructing the reflectivity function of the observed scenes. The TomoSAR regularization-based method utilizes the Constrained Least Squares (CLS) and the Weighted Constrained Least Squares (WCLS) as two efficient strategies for coming up with a well-posed solution to the prevalent ill-posed TomoSAR problem. They can assist SAR tomography to deal with the possible impairing issues arising from low numbers and the distribution of baselines. This advantage helps enhance the accuracy of the results. The experimental results are investigated in terms of completeness and correctness measures to evaluate the feasibility and reliability of the proposed method. The proposed method for height change detection has been performed on two datasets acquired by the TerraSAR-X sensor in 2013 and the Sentinel-1 sensor in 2022 over Tehran City, Iran. The final results demonstrate that the proposed algorithm can detect height changes with good feasibility and reliability in terms of completeness and correctness of better than 84% and 77%, respectively.

Two-Dimensional and 3D Change Detection in Urban Area Using Very High-Resolution Satellite Data and Impact of Urbanization over LST and NDVI

Two-Dimensional and 3D Change Detection in Urban Area Using Very High-Resolution Satellite Data and Impact of Urbanization over LST and NDVI

Spatio-temporal Change Detection of Built-Up Areas with Sentinel-1 SAR Data Using Random Forest Classification Arnavutköy Istanbul

As one of the most populated cities in Turkiye and the world, the Istanbul metropolis has always attracted the masses. Arnavutköy Town has become one of the critical points of Istanbul City with increasing built-up areas (BAs). The spatial-temporal change detection of the expansion of the BA of this district is essential data on behalf of Istanbul City. This research aims to determine urban areas expansion zones, also defined as the BA footprint, from Sentinel-1 radar data. The determination of Sentinel-1A data of the urban area change detection encountered in Arnavutköy Town between 2018-2021 with Random Forest (RF) classification machine learning algorithm is investigated in this study. The changes experienced with the spatial-temporal data were determined, and causes and effects were investigated. In order to visually compare the Normalized Difference Built-up Index (NDBI) and optical Sentinel-2A's false color urban RGB composite, which is a distinct data format, the processes have been proved. SAR satellite data was found to be more appropriate than optical satellite data since not being affected by atmospheric conditions for extracting BAs with remotely sensed data.

STCD-EffV2T Unet: Semi Transfer Learning EfficientNetV2 T-Unet Network for Urban/Land Cover Change Detection Using Sentinel-2 Satellite Images

Change detection in urban areas can be helpful for urban resource management and smart city planning. The effects of human activities on the environment and ground have gained momentum over the past decades, causing remote sensing data sources analysis (such as satellite images) to become an option for swift change detection in the environment and urban areas. We proposed a semi-transfer learning method of EfficientNetV2 T-Unet (EffV2 T-Unet) that combines the effectiveness of composite scaled EfficientNetV2 T as the first path or encoder for feature extraction and convolutional layers of Unet as the second path or decoder for reconstructing the binary change map. In the encoder path, we use EfficientNetV2 T, which was trained by the ImageNet dataset. In this research, we employ two datasets to evaluate the performance of our proposed method for binary change detection. The first dataset is Sentinel-2 satellite images which were captured in 2017 and 2021 in urban areas of northern Iran. The second one is the Onera Satellite Change Detection dataset (OSCD). The performance of the proposed method is compared with YoloX-Unet families, ResNest-Unet families, and other well-known methods. The results demonstrated our proposed method’s effectiveness compared to other methods. The final change map reached an overall accuracy of 97.66%.

Change Target Extraction Based on Scale-Adaptive Difference Image and Morphology Filter for KOMPSAT-5

Multitemporal synthetic aperture radar (SAR) images have been widely used for change detection and monitoring of the environment owing to their competency under all weather conditions. However, owing to speckle backgrounds and strong reflections, change detection in urban areas is challenging. In this study, to automatically extract changed objects, we developed a model that integrated change detection and object extraction in multiple Korean Multi-Purpose Satellite-5 (KOMPSAT-5) images. Initially, two arbitrary L1A-level SAR images were input into the proposed model, and after pre-processing, such as radio calibration and coordinate system processing, change detection was performed. Subsequently, the desired targets were automatically extracted from the change detection results. Finally, the model obtained images of the extraction targets and metadata, such as date and location. Noise was removed by applying scale-adaptive modification to the generated difference image during the change detection process, and the detection accuracy was improved by emphasizing the occurrence of the change. After polygonizing the pixel groups of the change detection map in the target extraction process, the morphology-based object filtering technique was applied to minimize the false detection rate. As a result of the proposed approach, the changed objects in the KOMPSAT-5 images were automatically extracted with 90% accuracy.

Three-Dimensional Change Detection in Urban Areas Based on Complementary Evidence Fusion

With the acceleration of urbanization, it is essential to carry out change detection (CD) and obtain surface change information in urban areas. In the early stages, the spectral information of remote sensing images was used as a change index to capture the spectral and texture changes of ground objects in a two-dimensional plane. However, due to the dense buildings in urban areas, shadows and occlusions can be easily formed, and spectral information is sensitive to the imaging environment, such as the illumination, atmospheric conditions, and imaging angles, so that the detection results based on remote sensing images can often be incomplete. Most changes include not only 2-D plane changes but also 3-D elevation changes. Compared with spectral information, the elevation is more stable and more resistant to interference. Therefore, the fusion of remote sensing image and digital surface model (DSM) data has the potential to be used to detect the changes in urban areas. In this article, we propose a complementary evidence fusion 3-D CD framework based on the Dempster–Shafer theory (CDST). In this framework, DSM and normalized difference vegetation index (NDVI) data are combined using a complementary evidence combination rule. The DSM data can effectively overcome the impact of shadows, and the NDVI data can capture the relevant changes of height-insensitive ground objects, such as vegetation and water. When mapping the basic probability assignment (BPA) of the difference image (DI), prior knowledge is used to ensure that the BPA is not affected by the data distribution. Since DSM and remote sensing image data are heterogeneous data, there is a high degree of conflict when representing the change information characteristics of specific areas. For example, the change between grassland and road is small in elevation but significant in the spectral details, and the traditional Dempster’s combination rule no longer applies. The proposed CDST framework uses a complementary evidence combination rule, which can effectively alleviate the conflicts between the evidence sources and improve the integrity of the detected changes. The experimental results obtained on real datasets confirm that the proposed method does indeed perform well.

Analysis of urban development on land cover changes of three cities of Gujarat state, India

Urbanization generally serves as a key navigator of the economic growth and development of the country. There is a need for fast and accurate urban planning to accommodate more and more people in the city area. Remote sensing technology has been used for planning the expansion and design of city areas. A novel machine learning (ML) classifier formed by combining AdaBoost and extra trees algorithm have been investigated for change detection in the urban area of three cities in the Gujarat region of India. Using Indian Remote Sensing (IRS) Resourcesat-2 LISS IV satellite images, the performance of the object-based AdaBoosted extra trees classifier (ABETC) in terms of overall accuracy (OA) and kappa coefficient (KC) for urban area change detection was compared to benchmarked object-based algorithms. As the first step in object-based classification (OBC), the Shepherd segmentation algorithm was used to segment satellite images. For all three cities, the object-based ABETC demonstrated the highest efficiency when compared to conventional classifiers. The rise in the built-up area of Ahmedabad city has been noted by 87.39 sq km from the year 2011 to 2020 showing the urban development of the city. This increase in the built-up area of Ahmedabad was compensated by the depletion of 30.26 sq. km. vegetation area, and 57.13 sq. km. of open land class. The built-up area of Vadodara and Rajkot city has been enlarged by 17.24 sq km and 6.79 sq km respectively. The highest OA of 96.04% and KC of 0.94 has been noted for a satellite image of Vadodara city with a novel object based ABETC algorithm.

An Application of Deep Learning in Remote Sensing: Automatic Change Detection in Urban Area

Change detection of earth’s appearance is a core task for urban management, whose main goal is to identify the change of physical materials via comparing remote sensed images (satellite images, aerial images etc.) of many time epochs. For instance, due to urbanization bare soil can be changed to building; river or lake can be filled up by soil for greening purpose. Recently, deep learning based methods for change detection have been widely applied. The standard pipeline here is: given two images of two epochs and the corresponding changed binary labels, they are feed into a deep learning model to learn the change incidents. However, this kind of change detection focuses solely separating change from no-change, yet ignores the information of “from-to”. For example, the change from soil to building is different to the change from water to soil, but in the standard pipeline they are taken as the same changed incident. In this paper, we propose a deep learning method to tackle this problem, i.e. not only detecting the change incidents, but also predict the change types (the “from-to” information). Our methods are evaluated on the competition dataset released by the SenseTime, and achieve promising detection results i.e. >70% in terms of OA.

Time series analysis of VIIRS-DNB nighttime lights imagery for change detection in urban areas: A case study of devastation in Puerto Rico from hurricanes Irma and Maria

Brightness of nighttime lights (NTL) collected by the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) is compatible across different times of images thanks to the on-board calibration system. However, the NTL radiance observed by the S-NPP VIIRS shows clear seasonality corresponding to the seasonal changes in the albedo of land surface. Additionally, the existence of many uncertain factors (e.g. complex atmospheric conditions) renders it inappropriate to directly use the NTL radiances to derive changes on the ground. In this study, we adopt a statistical procedure of time series analysis, namely seasonal and trend decomposition using Loess (STL), to model the time series observations of NTL brightness by decomposing the observations into three separable time series components (i.e. trend, seasonality, and remainder). Based on the time series model, forecast can be made for short-term future with confidence measure, and by comparing the model forecast with observed NTL brightness, significant changes can then be detected at pixel levels. We applied this method to the Puerto Rico area to detect and assess the damages caused by Hurricanes Irma and Maria, and to monitor the recovery after the disaster. Our results show that the proposed method successfully captures the changes of NTL brightness due to the damage of the hurricanes and general economic decline. Moreover, we also find that after removing the seasonal and remainder components, the time series of NTL image can more accurately reflect the temporal trends of economic status in Puerto Rico.

3D CHANGE DETECTION IN URBAN AREAS BASED ON DCNN USING A SINGLE IMAGE

Abstract. In this paper, a novel approach is proposed for 3D change detection in urban areas using only a single satellite images. To this purpose, a dense convolutional neural network (DCNN) is utilized in order to estimate a digital surface model (DSM) from a single image. In this regard, a densely connected convolutional network is employed for feature extraction and an upsampling method based on dilated convolution is employed for estimating the height values. The proposed DCNN is trained using satellite and Light Detection and Ranging (LiDAR) data which are provided in 2012 from Isfahan, Iran. Subsequently, the trained network is utilized in order to estimate DSM of a single satellite image that is provided in 2006. Finally, the changed areas are detected by subtracting the estimated DSMs. Evaluating the accuracy of the detected changed areas indicates 66.59, 72.90 and 67.90 for correctness, completeness, and kappa, respectively.

Change detection in urban areas from remote sensing data: a multidimensional classification scheme

ABSTRACT Change detection (CD) from remote sensing data is a very challenging research problem, especially when we analyse an urban scene. Urban scenes are composed of many different types of objects, both natural and man-made. The building class is one of the important and most complex classes to analyse, important because it is useful for so many applications and complex because it exhibits many changes due to human activity and natural catastrophes. For these reasons, we focus our study on building change detection (BCD). In this paper we propose a classification scheme for BCD research according to several important dimensions including objective, input data, temporal resolution, analysis unit, target output unit, building features, processing technique, change categories, and assessment of results. This classification scheme can guide practitioners in choosing appropriate change detection methods to achieve their goals as well as informing new research efforts. Based on this multidimensional characterisation of BCD, we offer a number of suggestions for further work to be done in this field.

A Novel Change Detection Approach for Multi-Temporal High-Resolution Remote Sensing Images Based on Rotation Forest and Coarse-to-Fine Uncertainty Analyses

In the process of object-based change detection (OBCD), scale is a significant factor related to extraction and analyses of subsequent change data. To address this problem, this paper describes an object-based approach to urban area change detection (CD) using rotation forest (RoF) and coarse-to-fine uncertainty analyses of multi-temporal high-resolution remote sensing images. First, highly homogeneous objects with consistent spatial positions are identified through vector-raster integration and multi-scale fine segmentation. The multi-temporal images are stacked and segmented under the constraints of a historical land use vector map using a series of optimal segmentation scales, ranging from coarse to fine. Second, neighborhood correlation image analyses are performed to highlight pixels with high probabilities of being changed or unchanged, which can be used as a prerequisite for object-based analyses. Third, based on the coarse-to-fine segmentation and pixel-based pre-classification results, change possibilities are calculated for various objects. Furthermore, changed and unchanged objects identified at different scales are automatically selected to serve as training samples. The spectral and texture features of each object are extracted. Finally, uncertain objects are classified using the RoF classifier. Multi-scale classification results are combined using a majority voting rule to generate the final CD results. In experiments using two pairs of real high-resolution remote sensing datasets, our proposed approach outperformed existing methods in terms of CD accuracy, verifying its feasibility and effectiveness.

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.

Separate segmentation of multi-temporal high-resolution remote sensing images for object-based change detection in urban area

High-resolution (HR) remote sensing images present geometric details of land surface. Object-based change detection (OBCD) provides an effective solution to reveal detailed changes of geographic objects in HR images. In OBCD, the separate segmentation strategy holds the potential of revealing specific object-to-object changes, but it is difficult to establish spatial correspondence between distinct multi-temporal segments. To overcome this difficulty, we proposed to first detect multi-temporal changed objects based on separate segmentations and then to establish spatial correspondence between changed objects at different phases. Three separate segmentation strategies, named as SIISeg, SAISeg, and SAOSeg, are compared to indicate the importance of associating separate segmentation procedures for successive spatial correspondence establishment. The experiments of detecting building changes in urban area are performed to demonstrate the success, benefits, and potentials of establishing spatial correspondence for object-to-object change detection.

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.

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.

An Unsupervised Technique for Building Change Detection in Urban Area

unsupervised technique for building extraction in urban area is proposed, which is based on the use of morphological processing especially for building index. Extracting buildings is one of the most complex and challenging tasks as there exist a lot of inhomogeneity due to varying hierarchy. The variety of the type of the buildings and also the shapes of rooftops are very inconstant. This technique proposes a solution to the problem of automatic and unsupervised change detection of rooftop structures. The morphological operation is used to extract building feature with the use of modified morphological building index (MBI). Original Morphological Building Index (MBI) can extract interest building features only for multi-temporal high-resolution satellite image. But the proposed method is insensitive to the geometrical differences of buildings caused by different imaging conditions and is able to significantly reduce false alarms and also can detect upon low-resolution imagery. Image registration is applied after extracting building feature to overlay two images of the same scene. The effectiveness of the method is validated by comparing with MBI-based Change Vector Analysis (CVA) and Multivariate alteration detection (MAD) transformation.

Exploiting Regular Patterns to Group Persistent Scatterers in Urban Areas

Urban areas are often characterized by geometrically simple and repetitive patterns, in particular, in cases where settlements have been built-up from scratch in a well-planned manner, e.g., according to architectonic, economic, or sociopolitic constraints. This leads to preferred rectangular and regular alignment of objects like windows or balconies at facades for the majority of buildings in modern cities. In this paper, we show how this regularity can be exploited for the challenging task of synthetic aperture radar (SAR) scene description and demonstrate the applicability in case studies. We present a virtually parameter-free method to segment a persistent scatterer point cloud and fit optimal lattices to describe separate facades. Formulating the PS as nodes in a graph allows us to use spectral graph theory to distinguish lattices even when they are overlapping or disturbed due to layover. As a result, we obtain an object-based representation of the SAR data, which allows for many new applications in the field of building monitoring and change detection in urban areas.

Markovian Change Detection of Urban Areas Using Very High Resolution Complex SAR Images

In this letter, an innovative technique for change detection in urban areas using very high resolution synthetic aperture radar multichannel stacks is proposed. Instead of using the amplitude image, as in classical change detection approaches, the proposed technique uses the full complex image in a Markovian framework. The complex data are modeled using Markov random field hyperparameters, which are particular local parameters that take into account the spatial correlation between pixels. Starting from two data sets, the pre- and the postevent ones, the proposed algorithm, first, estimates the two hyperparameter maps and, then, compares the similarity between them. If a change occurs between the pre- and the postevent acquisitions, the statistical distribution of the hyperparameter maps will change. The maximum distance between the two obtained statistical distributions provides an index of changes. This sort of spatial correlation maps is computed using statistical estimation techniques, while the similarity comparison is computed using the two-step Kolmogorov-Smirnov statistic test. The algorithm is validated on simulated data and tested on real COSMO-SkyMed data acquired on the area of Naples, showing interesting and promising results.