scholarly journals An Urban Flooding Index for Unsupervised Inundated Urban Area Detection Using Sentinel-1 Polarimetric SAR Images

2021 ◽  
Vol 13 (22) ◽  
pp. 4511
Author(s):  
Hui Zhang ◽  
Zhixin Qi ◽  
Xia Li ◽  
Yimin Chen ◽  
Xianwei Wang ◽  
...  
Keyword(s):  
Urban Area ◽  
Urban Areas ◽  
Ground Truth ◽  
Detection Accuracy ◽  
Urban Flooding ◽  
Polarimetric Sar ◽  
Sar Images ◽  
Ground Truth Data ◽  
Optical Images ◽  
Coefficient Increase

Urban flooding causes a variation in radar return from urban areas. However, such variation has not been thoroughly examined for different polarizations because of the lack of polarimetric SAR (PolSAR) images and ground truth data simultaneously collected over flooded urban areas. This condition hinders not only the understanding of the effect mechanism of urban flooding under different polarizations but also the development of advanced methods that could improve the accuracy of inundated urban area detection. Using Sentinel-1 PolSAR and Jilin-1 high-resolution optical images acquired on the same day over flooded urban areas in Golestan, Iran, this study investigated the characteristics and mechanisms of the radar return changes induced by urban flooding under different polarizations and proposed a new method for unsupervised inundated urban area detection. This study found that urban flooding caused a backscattering coefficient increase (BCI) and interferometric coherence decrease (ICD) in VV and VH polarizations. Furthermore, VV polarization was more sensitive to the BCI and ICD than VH polarization. In light of these findings, the ratio between the BCI and ICD was defined as an urban flooding index (UFI), and the UFI in VV polarization was used for the unsupervised detection of flooded urban areas. The overall accuracy, detection accuracy, and false alarm rate attained by the UFI-based method were 96.93%, 91.09%, and 0.95%, respectively. Compared with the conventional unsupervised method based on the ICD and that based on the fusion of backscattering coefficients and interferometric coherences (FBI), the UFI-based method achieved higher overall accuracy. The performance of VV was evaluated and compared to that of VH in the flooded urban area detection using the UFI-, ICD-, and FBI-based methods, respectively. VV polarization produced higher overall accuracy than VH polarization in all the methods, especially in the UFI-based method. By using VV instead of VH polarization, the UFI-based method improved the detection accuracy by 38.16%. These results indicated that the UFI-based method improved flooded urban area detection by synergizing the BCI and ICD in VV polarization.

scholarly journals InSAR Multitemporal Data over Persistent Scatterers to Detect Floodwater in Urban Areas: A Case Study in Beletweyne, Somalia

2020 ◽  
Vol 13 (1) ◽  
pp. 37
Author(s):  
Luca Pulvirenti ◽  
Marco Chini ◽  
Nazzareno Pierdicca
Keyword(s):  
Urban Area ◽  
Urban Areas ◽  
The Other ◽  
Spatial Average ◽  
Calibration Data ◽  
Suburban Areas ◽  
Persistent Scatterers ◽  
Optical Images ◽  
Interferometric Phase ◽  
Multitemporal Data

A stack of Sentinel-1 InSAR data in an urban area where flood events recurrently occur, namely Beletweyne town in Somalia, has been analyzed. From this analysis, a novel method to deal with the problem of flood mapping in urban areas has been derived. The approach assumes the availability of a map of persistent scatterers (PSs) inside the urban settlement and is based on the analysis of the temporal trend of the InSAR coherence and the spatial average of the exponential of the InSAR phase in each PS. Both interferometric products are expected to have high and stable values in the PSs; therefore, anomalous decreases may indicate that floodwater is present in an urban area. The stack of Sentinel-1 data has been divided into two subsets. The first one has been used as a calibration set to identify the PSs and determine, for each PS, reference values of the coherence and the spatial average of the exponential of the interferometric phase under standard non-flooded conditions. The other subset has been used for validation purposes. Flood maps produced by UNOSAT, analyzing very-high-resolution optical images of the floods that occurred in Beletweyne in April–May 2018, October–November 2019, and April–May 2020, have been used as reference data. In particular, the map of the April–May 2018 flood has been used for training purposes together with the subset of Sentinel-1 calibration data, whilst the other two maps have been used to validate the products generated by applying the proposed method. The main product is a binary map of flooded PSs that complements the floodwater map of rural/suburban areas produced by applying a well-consolidated algorithm based on intensity data. In addition, a flood severity map that labels the different districts of Beletweyne, as not, partially, or totally flooded has been generated to consolidate the validation. The results have confirmed the effectiveness of the proposed method.

scholarly journals Towards Automatic Recognition of Wakes Generated by Dark Vessels in Sentinel-1 Images

2021 ◽  
Vol 13 (10) ◽  
pp. 1955
Author(s):  
Maria Daniela Graziano ◽  
Alfredo Renga
Keyword(s):  
Surface Film ◽  
A Priori ◽  
Ground Truth ◽  
Detection Performance ◽  
Sar Images ◽  
Ground Truth Data ◽  
Low Wind Speed ◽  
Maritime Surveillance ◽  
Strong Transition ◽  
Detection Capabilities

The recognition of wakes generated by dark vessels is a tremendous and interesting challenge in the field of maritime surveillance by Synthetic Aperture Radar (SAR) images. The paper aims at assessing the detection performance in different scenarios by processing Sentinel-1 SAR images along with ground truth data. Results confirm that the Radon-based approach is an effective technique for wake-based detection of dark vessels, and they lead to a deeper understanding of the effects of different sea and wind conditions. In general, the best applicative scenario is a marine image characterized by homogeneous sea clutter; the presence of natural surface film or strong transition from low wind speed areas to more windy zones worsen the detection performance. Nonetheless, the proposed approach features dark vessel detection capabilities by identifying their wakes, without any a priori knowledge of their positions.

A Digital Terrain Modeling Method in Urban Areas by the ICESat-2 (Generating precise terrain surface profiles from photon-counting technology)

2021 ◽  
Vol 87 (4) ◽  
pp. 237-248
Author(s):  
Nahed Osama ◽  
Bisheng Yang ◽  
Yue Ma ◽  
Mohamed Freeshah
Keyword(s):  
Urban Areas ◽  
Spatial Clustering ◽  
Photon Counting ◽  
Ground Surface ◽  
Ground Truth ◽  
Surface Model ◽  
Laser Altimeter ◽  
Ground Truth Data ◽  
Urban Scenes ◽  
Terrain Surface

The ICE, Cloud and land Elevation Satellite-2 (ICES at-2) can provide new measurements of the Earth's elevations through photon-counting technology. Most research has focused on extracting the ground and the canopy photons in vegetated areas. Yet the extraction of the ground photons from urban areas, where the vegetation is mixed with artificial constructions, has not been fully investigated. This article proposes a new method to estimate the ground surface elevations in urban areas. The ICES at-2 signal photons were detected by the improved Density-Based Spatial Clustering of Applications with Noise algorithm and the Advanced Topographic Laser Altimeter System algorithm. The Advanced Land Observing Satellite-1 PALSAR –derived digital surface model has been utilized to separate the terrain surface from the ICES at-2 data. A set of ground-truth data was used to evaluate the accuracy of these two methods, and the achieved accuracy was up to 2.7 cm, which makes our method effective and accurate in determining the ground elevation in urban scenes.

scholarly journals ANALYSIS OF SCATTERING COMPONENTS FROM FULLY POLARIMETRIC SAR IMAGES FOR IMPROVING ACCURACIES OF URBAN DENSITY ESTIMATION

2016 ◽  
Vol III-7 ◽  
pp. 219-226
Author(s):  
J. Susaki
Keyword(s):  
Normal Distribution ◽  
Urban Areas ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Polarimetric Sar ◽  
Sar Images ◽  
Polarimetric Synthetic Aperture Radar ◽  
Urban Density ◽  
Concrete Blocks ◽  
Image Pixels

In this paper, we analyze probability density functions (PDFs) of scatterings derived from fully polarimetric synthetic aperture radar (SAR) images for improving the accuracies of estimated urban density. We have reported a method for estimating urban density that uses an index <i>T</i><sub><i>v</i>+<i>c</i></sub> obtained by normalizing the sum of volume and helix scatterings <i>P</i><sub><i>v</i>+<i>c</i></sub>. Validation results showed that estimated urban densities have a high correlation with building-to-land ratios (Kajimoto and Susaki, 2013b; Susaki et al., 2014). While the method is found to be effective for estimating urban density, it is not clear why <i>T</i><sub><i>v</i>+<i>c</i></sub> is more effective than indices derived from other scatterings, such as surface or double-bounce scatterings, observed in urban areas. In this research, we focus on PDFs of scatterings derived from fully polarimetric SAR images in terms of scattering normalization. First, we introduce a theoretical PDF that assumes that image pixels have scatterers showing random backscattering. We then generate PDFs of scatterings derived from observations of concrete blocks with different orientation angles, and from a satellite-based fully polarimetric SAR image. The analysis of the PDFs and the derived statistics reveals that the curves of the PDFs of <i>P</i><sub><i>v</i>+<i>c</i></sub> are the most similar to the normal distribution among all the scatterings derived from fully polarimetric SAR images. It was found that <i>T</i><sub><i>v</i>+<i>c</i></sub> works most effectively because of its similarity to the normal distribution.

scholarly journals CHANGING THE PRODUCTION PIPELINE – USE OF OBLIQUE AERIAL CAMERAS FOR MAPPING PURPOSES

2016 ◽  
Vol XLI-B4 ◽  
pp. 631-637
Author(s):  
K. Moe ◽  
I. Toschi ◽  
D. Poli ◽  
F. Lago ◽  
C. Schreiner ◽  
...  
Keyword(s):  
Urban Areas ◽  
Point Cloud ◽  
Reference Data ◽  
Ground Truth ◽  
Building Extraction ◽  
Camera System ◽  
Ground Truth Data ◽  
Production Pipeline ◽  
Dense Image ◽  
Oblique Images

This paper discusses the potential of current photogrammetric multi-head oblique cameras, such as UltraCam Osprey, to improve the efficiency of standard photogrammetric methods for surveying applications like inventory surveys and topographic mapping for public administrations or private customers. <br><br> In 2015, Terra Messflug (TM), a subsidiary of Vermessung AVT ZT GmbH (Imst, Austria), has flown a number of urban areas in Austria, Czech Republic and Hungary with an UltraCam Osprey Prime multi-head camera system from Vexcel Imaging. In collaboration with FBK Trento (Italy), the data acquired at Imst (a small town in Tyrol, Austria) were analysed and processed to extract precise 3D topographic information. The Imst block comprises 780 images and covers an area of approx. 4.5 km by 1.5 km. Ground truth data is provided in the form of 6 GCPs and several check points surveyed with RTK GNSS. Besides, 3D building data obtained by photogrammetric stereo plotting from a 5 cm nadir flight and a LiDAR point cloud with 10 to 20 measurements per m² are available as reference data or for comparison. The photogrammetric workflow, from flight planning to Dense Image Matching (DIM) and 3D building extraction, is described together with the achieved accuracy. For each step, the differences and innovation with respect to standard photogrammetric procedures based on nadir images are shown, including high overlaps, improved vertical accuracy, and visibility of areas masked in the standard vertical views. Finally the advantages of using oblique images for inventory surveys are demonstrated.

scholarly journals An Unsupervised Port Detection Method in Polarimetric SAR Images Based on Three-Component Decomposition and Multi-Scale Thresholding Segmentation

2022 ◽  
Vol 14 (1) ◽  
pp. 205
Author(s):  
Chun Liu ◽  
Jian Yang ◽  
Jiangbin Zheng ◽  
Xuan Nie
Keyword(s):  
Detection Method ◽  
Ground Truth ◽  
Large Area ◽  
Polarimetric Sar ◽  
Sar Images ◽  
Volume Scattering ◽  
Multi Scale ◽  
Component Decomposition ◽  
Union Section ◽  
Strong Scattering

It is difficult to detect ports in polarimetric SAR images due to the complicated components, morphology, and coastal environment. This paper proposes an unsupervised port detection method by extracting the water of the port based on three-component decomposition and multi-scale thresholding segmentation. Firstly, the polarimetric characteristics of the port water are analyzed using modified three-component decomposition. Secondly, the volume scattering power and the power ratio of the double-bounce scattering power to the volume scattering power (PRDV) are used to extract the port water. Water and land are first separated by a global thresholding segmentation of the volume scattering power, in which the sampling region used for the threshold calculation is automatically selected by a proposed homogeneity measure. The interference water regions in the ports are then separated from the water by segmenting the PRDV using the multi-scale thresholding segmentation method. The regions of interest (ROIs) of the ports are then extracted by determining the connected interference water regions with a large area. Finally, ports are recognized by examining the area ratio of strong scattering pixels to the land in the extracted ROIs. Seven single quad-polarization SAR images acquired by RADARSAT-2 covering the coasts of Dalian, Zhanjiang, Fujian, Tianjin, Lingshui, and Boao in China and Berkeley in America are used to test the proposed method. The experimental results show that all ports are correctly and quickly detected. The false alarm rates are zero, the intersection of union section (IoU) indexes between the detected port and the ground truth can reach 75%, and the average processing time can be less than 100 s.

scholarly journals POI Mining for Land Use Classification: A Case Study

2020 ◽  
Vol 9 (9) ◽  
pp. 493 ◽  
Author(s):  
Renato Andrade ◽  
Ana Alves ◽  
Carlos Bento
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Areas ◽  
Smart Cities ◽  
Ground Truth ◽  
Land Use Classification ◽  
Data Types ◽  
Ground Truth Data ◽  
Functional Roles ◽  
Artificial Neural Network Ann

The modern planning and management of urban spaces is an essential topic for smart cities and depends on up-to-date and reliable information on land use and the functional roles of the places that integrate urban areas. In the last few years, driven by the increased availability of geo-referenced data from social media, embedded sensors, and remote sensing images, various techniques have become popular for land use analysis. In this paper, we first highlight and discuss the different data types and methods usually adopted in this context, as well as their purposes. Then, based on a systematic state-of-the-art study, we focused on exploring the potential of points of interest (POIs) for land use classification, as one of the most common categories of crowdsourced data. We developed an application to automatically collect POIs for the study area, creating a dataset that was used to generate a large number of features. We used a ranking technique to select, among them, the most suitable features for classifying land use. As ground truth data, we used CORINE Land Cover (CLC), which is a solid and reliable dataset available for the whole European territory. It was used an artificial neural network (ANN) in different scenarios and our results reveal values of more than 90% for the accuracy and F-score in one experiment performed. Our analysis suggests that POI data have promising potential to characterize geographic spaces. The work described here aims to provide an alternative to the current methodologies for land use and land cover (LULC) classification, which are usually time-consuming and depend on expensive data types.

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