scholarly journals Urban objects detection from C-band synthetic aperture radar (SAR) satellite images through simulating filter properties

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Deepak Kumar
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Microwave Remote Sensing ◽  
Google Earth ◽  
Alternative Methods ◽  
Synthetic Aperture ◽  
Radar Images ◽  
Radar Sensing ◽  
Objects Detection ◽  
Aperture Radar

AbstractSatellite-based remote sensing has a key role in the monitoring earth features, but due to flaws like cloud penetration capability and selective duration for remote sensing in traditional remote sensing methods, now the attention has shifted towards the use of alternative methods such as microwave or radar sensing technology. Microwave remote sensing utilizes synthetic aperture radar (SAR) technology for remote sensing and it can operate in all weather conditions. Previous researchers have reported about effects of SAR pre-processing for urban objects detection and mapping. Preparing high accuracy urban maps are critical to disaster planning and response efforts, thus result from this study can help to users on the required pre-processing steps and its effects. Owing to the induced errors (such as calibration, geometric, speckle noise) in the radar images, these images are affected by several distortions, therefore these distortions need to be processed before any applications, as it causes issues in image interpretation and these can destroy valuable information about shapes, size, pattern and tone of various desired objects. The present work aims to utilize the sentinel-1 SAR datasets for urban studies (i.e. urban object detection through simulation of filter properties). The work uses C-band SAR datasets acquired from Sentinel-1A/B sensor, and the Google Earth datasets to validate the recognized objects. It was observed that the Refined-Lee filter performed well to provide detailed information about the various urban objects. It was established that the attempted approach cannot be generalised as one suitable method for sensing or identifying accurate urban objects from the C-band SAR images. Hence some more datasets in different polarisation combinations are required to be attempted.

scholarly journals Earth Environmental Monitoring Using Multi-Temporal Synthetic Aperture Radar: A Critical Review of Selected Applications

2021 ◽  
Vol 13 (4) ◽  
pp. 604
Author(s):  
Donato Amitrano ◽  
Gerardo Di Martino ◽  
Raffaella Guida ◽  
Pasquale Iervolino ◽  
Antonio Iodice ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Microwave Remote Sensing ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Special Focus ◽  
Arid Environments ◽  
Synthetic Aperture Radar Data ◽  
Multi Temporal ◽  
Aperture Radar

Microwave remote sensing has widely demonstrated its potential in the continuous monitoring of our rapidly changing planet. This review provides an overview of state-of-the-art methodologies for multi-temporal synthetic aperture radar change detection and its applications to biosphere and hydrosphere monitoring, with special focus on topics like forestry, water resources management in semi-arid environments and floods. The analyzed literature is categorized on the base of the approach adopted and the data exploited and discussed in light of the downstream remote sensing market. The purpose is to highlight the main issues and limitations preventing the diffusion of synthetic aperture radar data in both industrial and multidisciplinary research contexts and the possible solutions for boosting their usage among end-users.

scholarly journals GEOPHYSICAL PARAMETER RETRIEVAL FOR MICROWAVE C BAND SYNTHETIC APERTURE RADAR (SAR) DATASET USING INTEGRAL EQUATION MODEL

2018 ◽  
Vol XLII-5 ◽  
pp. 689-692
Author(s):  
S. B. Sayyad ◽  
M. A. Shaikh ◽  
S. B. Kolhe ◽  
P. W. Khirade
Keyword(s):  
Remote Sensing ◽  
Integral Equation ◽  
Synthetic Aperture Radar ◽  
Statistical Parameter ◽  
Microwave Remote Sensing ◽  
Equation Model ◽  
Synthetic Aperture ◽  
Geophysical Parameter ◽  
Planetary Bodies ◽  
Aperture Radar

<p><strong>Abstract.</strong> The microwave remote sensing is highly useful, as it provides synoptic observation of the Earth’s surface or planetary bodies, regardless of day or night and the atmospheric conditions, propagation through ionosphere with minimum loss. One of the best microwave technology for imaging system is the Synthetic Aperture Radar (SAR) remote sensing. The microwave SAR currently represents the best approach for obtaining spatially distributed geophysical parameter present on the Earth’s surface or planetary bodies. In the present work, geophysical parameters <i>viz.</i>, Soil Moisture, Surface Roughness, Dielectric Constant (&amp;epsilon;) and Backscattering Coefficients (&amp;sigma;<sup>0</sup>) will be retrieved. The modelling makes the process of estimating information beyond the real observation range for data interpretation. In the present paper most widely used modelling techniques for the microwave SAR dataset is an Integral Equation Model (IEM) which is implemented for above said geophysical parameters retrieval. The aim of the present work is to estimate accurate, reliable and skillful measurements of geophysical parameters from the microwave SAR dataset. In the present study microwave C band SAR dataset is used. The overall processing was done by using PolSARPro Ver. 5.0 software. In the present work, geophysical parameters are measured with the help IEM modelling, the statistical parameter and occurrence plane, estimated from the microwave SAR image, which was very helpful for retrieving geophysical parameters. From the overall paper work, it was concluded that the IEM modelling is a one of the realistic modelling methods for retrieving geophysical parameters for microwave C band SAR dataset.</p>

scholarly journals Mapping up-to-Date Paddy Rice Extent at 10 M Resolution in China through the Integration of Optical and Synthetic Aperture Radar Images

2018 ◽  
Vol 10 (8) ◽  
pp. 1200 ◽  
Author(s):  
Xin Zhang ◽  
Bingfang Wu ◽  
Guillermo Ponce-Campos ◽  
Miao Zhang ◽  
Sheng Chang ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
High Performance ◽  
Large Scale ◽  
Water Source ◽  
Google Earth ◽  
Hunan Province ◽  
Synthetic Aperture ◽  
Radar Images ◽  
Indirect Information ◽  
Aperture Radar

Rice is a staple food in East Asia and Southeast Asia—an area that accounts for more than half of the world’s population, and 11% of its cultivated land. Studies on rice monitoring can provide direct or indirect information on food security, and water source management. Remote sensing has proven to be the most effective method for the large-scale monitoring of croplands, by using temporary and spectral information. The Google Earth Engine (GEE) is a cloud-based platform providing access to high-performance computing resources for processing extremely large geospatial datasets. In this study, by leveraging the computational power of GEE and a large pool of satellite and other geophysical data (e.g., forest and water extent maps, with high accuracy at 30 m), we generated the first up-to-date rice extent map with crop intensity, at 10 m resolution in the three provinces with the highest rice production in China (the Heilongjiang, Hunan and Guangxi provinces). Optical and synthetic aperture radar (SAR) data were monthly and metric composited to ensure a sufficient amount of up-to-date data without cloud interference. To remove the common confounding noise in the pixel-based classification results at medium to high resolution, we integrated the pixel-based classification (using a random forest classifier) result with the object-based segmentation (using a simple linear iterative clustering (SLIC) method). This integration resulted in the rice planted area data that most closely resembled official statistics. The overall accuracy was approximately 90%, which was validated by ground crop field points. The F scores reached 87.78% in the Heilongjiang Province for monocropped rice, 89.97% and 80.00% in the Hunan Province for mono- and double-cropped rice, respectively, and 88.24% in the Guangxi Province for double-cropped rice.

scholarly journals Ship Detection Based on Information Theory and Segmentation from Synthetic Aperture Radar (SAR) Images

2020 ◽  
Vol 8 (6) ◽  
pp. 2513-2517
Keyword(s):  
Remote Sensing ◽  
Synthetic Aperture Radar ◽  
Satellite Images ◽  
Synthetic Aperture ◽  
Morphological Operations ◽  
Sar Images ◽  
Ship Detection ◽  
Radar Images ◽  
Vessel Detection ◽  
Aperture Radar

Ship detection is a procedure which asserts in fields such as ocean and sea management, vessel detection, marine superintendence, and rein, and also can be applied to exclude extralegal actions. Remote sensing can be utilized as a potential tool for zonular and universal monitoring to attain the forenamed goals. Among the radar images, the precious datum from Synthetic Aperture Radar (SAR) is playing a serious duty in remote sensing. Howsoever, vessel detecting in heterogeneous and strong clutter is still a question in this regard. The letter points to a ship detection scheme for SAR images exploiting a segmentation-based morphological operation using entropy. In the presented scheme, the morphological operations are adopted to intercept the background and foreground in the satellite images. The method was implemented and tested on the homogenous, heterogeneous and strong clutter SAR images and the results are promising and showing that the proposed method can improve the vessel detection from homogenous and heterogeneous and strong clutter satellite images

Change Detection and Flood Water Mapping Using Sentinel-1A Synthetic Aperture Radar Images

2019 ◽  
Vol 16 (8) ◽  
pp. 3544-3549
Author(s):  
N. Anusha ◽  
B. Bharathi
Keyword(s):  
Synthetic Aperture Radar ◽  
Uttar Pradesh ◽  
Google Earth ◽  
Water Levels ◽  
Control Measures ◽  
Synthetic Aperture ◽  
Radar Images ◽  
Sar Data ◽  
Specular Reflector ◽  
Aperture Radar

Prodigious flooding in the state of Uttar Pradesh, India during the month of August 2017 was induced by heavy rainfall, causing water levels in several rivers to cross the danger mark bringing normal life to a standstill. The peculiar rainfall pattern in India makes it highly vulnerable to floods. Demand for crisis information, for instance, natural disasters like severe flood events has increased. A simple but effective method is proposed in this study to find the areas that are affected due to floods, to detect the changes and for flood mapping. These indicators were derived from the Sentinel-1A Synthetic Aperture Radar (SAR) data by taking the crisis and archive images. An open flood surface can be detected easily in SAR data as it acts as a specular reflector that scatters the energy away from the sensor, causing relatively dark pixels of low backscattered SAR data. In contrast, the surrounding non-water areas usually exhibit a higher return due to surface roughness. Red, Green, Blue (RGB) composite is made for highlighting the flooded areas and for detecting changes by combining both archive and crisis images. Finally the flood map is compared with the optical imagery on the Google earth by integrating the resultant RGB composite image on the Google earth. Identification of the flood-prone areas is crucial to action the appropriate control measures in the flood-affected regions.

Sign in / Sign up

Export Citation Format

Share Document