scholarly journals A Low-Cost Active Reflector for Interferometric Monitoring Based on Sentinel-1 SAR Images

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2008
Author(s):  
Guido Luzi ◽  
Pedro F. Espín-López ◽  
Fermín Mira Pérez ◽  
Oriol Monserrat ◽  
Michele Crosetto
Keyword(s):  
Urban Areas ◽  
Low Cost ◽  
Cost Benefit ◽  
Weather Conditions ◽  
Severe Weather ◽  
Synthetic Aperture ◽  
Stable Phase ◽  
Dense Network ◽  
Sar Images ◽  
Active Reflector

The effectiveness of radar interferometric techniques in non-urban areas can often be compromised due to the lack of stable natural targets. This drawback can be partially compensated through the installation of reference targets, characterized by a bright and stable radar response. The installation of passive corner reflectors (PCR) often represents a valid aid, but these objects are usually cumbersome, and suffer from severe weather conditions; furthermore, the installation of a PCR can be difficult and costly, especially in places with hard accessibility. Active reflectors (AR) represent a less cumbersome alternative to PCRs, while still providing a stable phase response. This paper describes the design, implementation, and test of an AR prototype, designed to operate with the Sentinel-1 synthetic aperture radar (SAR), aimed at providing a fair performance/cost benefit. These characteristics, obtained through a tradeoff between the use of off-the-shelf components and a simple architecture, can make the setup of a dense network (i.e., tens of devices) in the monitored areas feasible. The paper reports the design, implementation, and the analysis of different tests carried out in a laboratory, and in a real condition in the field, to illustrate AR reliability and estimate its phase stability.

scholarly journals Observed decreases in on-road CO<sub>2</sub> concentrations in Beijing during COVID-19 restrictions

2021 ◽  
Vol 21 (6) ◽  
pp. 4599-4614
Author(s):  
Di Liu ◽  
Wanqi Sun ◽  
Ning Zeng ◽  
Pengfei Han ◽  
Bo Yao ◽  
...  
Keyword(s):  
Urban Areas ◽  
Low Cost ◽  
Working Hours ◽  
Weather Conditions ◽  
Co2 Concentration ◽  
Mobile Platforms ◽  
Local Standard ◽  
Transportation Emissions ◽  
Enhancement Method ◽  
The Difference

Abstract. To prevent the spread of the COVID-19 epidemic, restrictions such as “lockdowns” were conducted globally, which led to a significant reduction in fossil fuel emissions, especially in urban areas. However, CO2 concentrations in urban areas are affected by many factors, such as weather, biological sinks and background CO2 fluctuations. Thus, it is difficult to directly observe the CO2 reductions from sparse ground observations. Here, we focus on urban ground transportation emissions, which were dramatically affected by the restrictions, to determine the reduction signals. We conducted six series of on-road CO2 observations in Beijing using mobile platforms before (BC), during (DC) and after (AC) the implementation of COVID-19 restrictions. To reduce the impacts of weather conditions and background fluctuations, we analyze vehicle trips with the most similar weather conditions possible and calculated the enhancement metric, which is the difference between the on-road CO2 concentration and the “urban background” CO2 concentration measured at the tower of the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences. The results showed that the DC CO2 enhancement was decreased by 41 (±1.3) parts per million (ppm) and 26 (±6.2) ppm compared to those for the BC and AC trips, respectively. Detailed analysis showed that, during COVID-19 restrictions, there was no difference between weekdays and weekends during working hours (09:00–17:00 local standard time; LST). The enhancements during rush hours (07:00–09:00 and 17:00–20:00 LST) were almost twice those during working hours, indicating that emissions during rush hours were much higher. For DC and BC, the enhancement reductions during rush hours were much larger than those during working hours. Our findings showed a clear CO2 concentration decrease during COVID-19 restrictions, which is consistent with the CO2 emissions reductions due to the pandemic. The enhancement method used in this study is an effective method to reduce the impacts of weather and background fluctuations. Low-cost sensors, which are inexpensive and convenient, could play an important role in further on-road and other urban observations.

scholarly journals A Low-Cost Wireless Temperature Sensor: Evaluation for Use in Environmental Monitoring Applications

2014 ◽  
Vol 31 (4) ◽  
pp. 938-944 ◽  
Author(s):  
Duick T. Young ◽  
Lee Chapman ◽  
Catherine L. Muller ◽  
Xiao-Ming Cai ◽  
C. S. B. Grimmond
Keyword(s):  
Environmental Monitoring ◽  
Air Temperature ◽  
Urban Areas ◽  
Low Cost ◽  
Ambient Air ◽  
Platinum Resistance ◽  
Dense Network ◽  
Radiation Shield ◽  
Wide Range ◽  
Monitoring Applications

Abstract A wide range of environmental applications would benefit from a dense network of air temperature observations. However, with limitations of costs, existing siting guidelines, and risk of damage, new methods are required to gain a high-resolution understanding of spatiotemporal patterns of temperature for agricultural and urban meteorological phenomena such as the urban heat island. With the launch of a new generation of low-cost sensors, it is possible to deploy a network to monitor air temperature at finer spatial resolutions. This study investigates the Aginova Sentinel Micro (ASM) sensor with a custom radiation shield (together less than USD$150) that can provide secure near-real-time air temperature data to a server utilizing existing (or user deployed) Wi-Fi networks. This makes it ideally suited for deployment where wireless communications readily exist, notably urban areas. Assessment of the performance of the ASM relative to traceable standards in a water bath and atmospheric chamber show it to have good measurement accuracy with mean errors &lt;±0.22°C between −25° and 30°C, with a time constant in ambient air of 110 ±15 s. Subsequent field tests also showed the ASM (in the custom shield) had excellent performance (RMSE = 0.13°C) over a range of meteorological conditions relative to a traceable operational Met Office platinum resistance thermometer. These results indicate that the ASM and radiation shield are more than fit for purpose for dense network deployment in environmental monitoring applications at relatively low cost compared to existing observation techniques.

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 Ship Classification and Detection Based on CNN Using GF-3 SAR Images

2018 ◽  
Vol 10 (12) ◽  
pp. 2043 ◽  
Author(s):  
Mengyuan Ma ◽  
Jie Chen ◽  
Wei Liu ◽  
Wei Yang
Keyword(s):  
Large Scale ◽  
Weather Conditions ◽  
Synthetic Aperture ◽  
Feature Analysis ◽  
Single Shot ◽  
Target Classification ◽  
Sar Images ◽  
Different Types ◽  
Ship Classification ◽  
Fully Connected

Ocean surveillance via high-resolution Synthetic Aperture Radar (SAR) imageries has been a hot issue because SAR is able to work in all-day and all-weather conditions. The launch of Chinese Gaofen-3 (GF-3) satellite has provided a large number of SAR imageries, making it possible to marine targets monitoring. However, it is difficult for traditional methods to extract effective features to classify and detect different types of marine targets in SAR images. This paper proposes a convolutional neutral network (CNN) model for marine target classification at patch level and an overall scheme for marine target detection in large-scale SAR images. First, eight types of marine targets in GF-3 SAR images are labelled based on feature analysis, building the datasets for further experiments. As for the classification task at patch level, a novel CNN model with six convolutional layers, three pooling layers, and two fully connected layers has been designed. With respect to the detection part, a Single Shot Multi-box Detector with a multi-resolution input (MR-SSD) is developed, which can extract more features at different resolution versions. In order to detect different targets in large-scale SAR images, a whole workflow including sea-land segmentation, cropping with overlapping, detection with MR-SSD model, coordinates mapping, and predicted boxes consolidation is developed. Experiments based on the GF-3 dataset demonstrate the merits of the proposed methods for marine target classification and detection.

scholarly journals Small UAV-based SAR system using low-cost radar, position, and attitude sensors with onboard imaging capability

2021 ◽  
pp. 1-12
Author(s):  
Jan Svedin ◽  
Anders Bernland ◽  
Andreas Gustafsson ◽  
Eric Claar ◽  
John Luong
Keyword(s):  
Low Cost ◽  
Projection Algorithm ◽  
Synthetic Aperture ◽  
Back Projection ◽  
Data Link ◽  
Sar Images ◽  
Imaging Capability ◽  
Small Uav ◽  
Aerial Vehicle ◽  
High Resolution Images

Abstract This paper describes a small unmanned aerial vehicle (UAV)-based synthetic aperture radar (SAR) system using low-cost radar (5–6 GHz), position (GNSS/RTK) and attitude (IMU) sensors for the generation of high-resolution images. Measurements using straight as well as highly curved flight trajectories and varying flight speeds are presented, showing range and cross-range lobe-widths close to the theoretical limits. An analysis of the improvements obtained by the use of attitude angles (roll, pitch, and yaw), to correct for the relative offsets in antenna positions as the UAV moves, is included. A capability to generate SAR images onboard with the back-projection algorithm has been implemented using a GPU accelerated single-board computer. Generated images are transmitted to ground using a Wi-Fi data link.

scholarly journals Joint Sparsity for TomoSAR Imaging in Urban Areas Using Building POI and TerraSAR-X Staring Spotlight Data

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6888
Author(s):  
Lei Pang ◽  
Yanfeng Gai ◽  
Tian Zhang
Keyword(s):  
Urban Areas ◽  
Super Resolution ◽  
Likelihood Estimation ◽  
Satellite Orbit ◽  
Synthetic Aperture ◽  
Estimation Methods ◽  
Spectrum Estimation ◽  
Sar Images ◽  
Dense Point ◽  
Very High

Synthetic aperture radar (SAR) tomography (TomoSAR) can obtain 3D imaging models of observed urban areas and can also discriminate different scatters in an azimuth–range pixel unit. Recently, compressive sensing (CS) has been applied to TomoSAR imaging with the use of very-high-resolution (VHR) SAR images delivered by modern SAR systems, such as TerraSAR-X and TanDEM-X. Compared with the traditional Fourier transform and spectrum estimation methods, using sparse information for TomoSAR imaging can obtain super-resolution power and robustness and is only minorly impacted by the sidelobe effect. However, due to the tight control of SAR satellite orbit, the number of acquisitions is usually too low to form a synthetic aperture in the elevation direction, and the baseline distribution of acquisitions is also uneven. In addition, artificial outliers may easily be generated in later TomoSAR processing, leading to a poor mapping product. Focusing on these problems, by synthesizing the opinions of various experts and scholarly works, this paper briefly reviews the research status of sparse TomoSAR imaging. Then, a joint sparse imaging algorithm, based on the building points of interest (POIs) and maximum likelihood estimation, is proposed to reduce the number of acquisitions required and reject the scatterer outliers. Moreover, we adopted the proposed novel workflow in the TerraSAR-X datasets in staring spotlight (ST) work mode. The experiments on simulation data and TerraSAR-X data stacks not only indicated the effectiveness of the proposed approach, but also proved the great potential of producing a high-precision dense point cloud from staring spotlight (ST) data.

scholarly journals ASSESSING THE SUITABILITY OF SIMULATED SAR SIGNATURES OF DEBRIS FOR THE USAGE IN DAMAGE DETECTION

2016 ◽  
Vol XLI-B3 ◽  
pp. 877-881
Author(s):  
S. Kuny ◽  
H. Hammer ◽  
K. Schulz
Keyword(s):  
Urban Areas ◽  
Weather Conditions ◽  
False Alarms ◽  
Textural Features ◽  
Sar Image ◽  
Sar Images ◽  
Screening Process ◽  
Collapsed Buildings ◽  
Event Image ◽  
Rule Out

When disasters strike in urban areas, the most important issue is to direct rescue forces to the most heavily destroyed areas. SAR images, because of their independence from daylight and weather conditions, are the remote sensing tool of choice in these cases. However, often no pre-event image is available, so change detection cannot be performed. Thus, we aim at extracting areas of debris from a single post-event SAR image using textural features. We want to be independent of real samples of debris sites by using simulated SAR image chips. Previous work has shown that in this way we detect all major sites of debris, e.g. caused by collapsed buildings. However, the screening process also detects many other areas, especially high vegetation and gravel. In order to rule these areas out from the analysis, it is important to also simulate these classes of objects. The simulated chips can then be used in a classifier, specifically a random forest, to rule out these causes of false alarms.

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

2022 ◽  
Vol 14 (2) ◽  
pp. 245
Author(s):  
Yeonju Choi ◽  
Dochul Yang ◽  
Sanghyuck Han ◽  
Jaeung Han
Keyword(s):  
Change Detection ◽  
Urban Areas ◽  
Weather Conditions ◽  
Extraction Process ◽  
Detection Accuracy ◽  
Sar Images ◽  
Difference Image ◽  
Proposed Model ◽  
Target Extraction ◽  
Filtering Technique

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.

scholarly journals ESTIMATION OF SHIE GLACIER SURFACE MOVEMENT USING OFFSET TRACKING TECHNIQUE WITH COSMO-SKYMED IMAGES

2017 ◽  
Vol IV-2/W4 ◽  
pp. 493-497
Author(s):  
Q. Wang ◽  
W. Zhou ◽  
J. Fan ◽  
W. Yuan ◽  
H. Li ◽  
...  
Keyword(s):  
High Resolution ◽  
Maximum Velocity ◽  
Weather Conditions ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Glacier Surface ◽  
Surface Movement ◽  
Tracking Technique ◽  
Offset Tracking ◽  
Near Future

Movement is one of the most important characteristics of glaciers which can cause serious natural disasters. For this reason, monitoring this massive blocks is a crucial task. Synthetic Aperture Radar (SAR) can operate all day in any weather conditions and the images acquired by SAR contain intensity and phase information, which are irreplaceable advantages in monitoring the surface movement of glaciers. Moreover, a variety of techniques like DInSAR and offset tracking, based on the information of SAR images, could be applied to measure the movement. Sangwang lake, a glacial lake in the Himalayas, has great potentially danger of outburst. Shie glacier is situated at the upstream of the Sangwang lake. Hence, it is significant to monitor Shie glacier surface movement to assess the risk of outburst. In this paper, 6 high resolution COSMO-SkyMed images spanning from August to December, 2016 are applied with offset tracking technique to estimate the surface movement of Shie glacier. The maximum velocity of Shie glacier surface movement is 51&amp;thinsp;cm/d, which was observed at the end of glacier tongue, and the velocity is correlated with the change of elevation. Moreover, the glacier surface movement in summer is faster than in winter and the velocity decreases as the local temperature decreases. Based on the above conclusions, the glacier may break off at the end of tongue in the near future. The movement results extracted in this paper also illustrate the advantages of high resolution SAR images in monitoring the surface movement of small glaciers.

scholarly journals PRE-PROCESSES FOR URBAN AREAS DETECTION IN SAR IMAGES

2017 ◽  
Vol XLII-4/W6 ◽  
pp. 15-17
Author(s):  
S. Altay Açar ◽  
Ş. Bayır
Keyword(s):  
Experimental Study ◽  
Synthetic Aperture Radar ◽  
Urban Areas ◽  
Binary Image ◽  
Synthetic Aperture ◽  
Image Smoothing ◽  
Sar Images ◽  
Aperture Radar

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.

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