Pub Date : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358970
Savan Panchal, Tejas Turakhia, A. Chhabra, Rajesh C. Iyer
In this study, ground measurements of Black Carbon (BC) concentration were collected at different locations in Gandhinagar at two different times morning and evening, the capital city of Gujarat, during Post-monsoon season for year 2017 and 2018. Delta C and Absorption Angstrom Exponent (AAE) were calculated to identify the possible sources. During study, we observed a decrease of 51.7% in BC concentration in year 2018 compared to year 2017. Regions with heavy vehicle traffic shows high BC values especially during evening time. Occurrence of high Delta C and AAE are indicative of enhanced absorption in near-UV and low-visible wavelengths attributed to the presence of biomass burning and light absorbing particulate matter.
{"title":"Investigation on Black Carbon Concentration in Ambient Air Quality of Gandhinagar During Post Monsoon Period","authors":"Savan Panchal, Tejas Turakhia, A. Chhabra, Rajesh C. Iyer","doi":"10.1109/InGARSS48198.2020.9358970","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358970","url":null,"abstract":"In this study, ground measurements of Black Carbon (BC) concentration were collected at different locations in Gandhinagar at two different times morning and evening, the capital city of Gujarat, during Post-monsoon season for year 2017 and 2018. Delta C and Absorption Angstrom Exponent (AAE) were calculated to identify the possible sources. During study, we observed a decrease of 51.7% in BC concentration in year 2018 compared to year 2017. Regions with heavy vehicle traffic shows high BC values especially during evening time. Occurrence of high Delta C and AAE are indicative of enhanced absorption in near-UV and low-visible wavelengths attributed to the presence of biomass burning and light absorbing particulate matter.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"8 1","pages":"197-200"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84059648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358951
Dhanashri S. Kanade, V. S. K. Vanama, S. Shitole
Globally, 55% of the population lives in urban areas in 2018, and this number is expected to hit 68% by 2050. Earth Observation (EO) images based mapping of the urban regions is a critical parameter in the sustainable urban planning process. In recent years, rapid urban growth is experienced in the coastal metropolitan city of India-Chennai. The two land regions, having heterogeneous land uses, as high-rise high-density and medium-rise low-density of the Chennai city are taken as study area. The fully-polarimetric L-band ALOS-2 Synthetic Aperture Radar (SAR) data is used for rapid identification of the urban regions. With respect to this, a comparative assessment of the two supervised classification algorithms such as Wishart and Support Vector Machine (SVM) is presented. The same training data set is used for both algorithms, and a confusion matrix is created algorithm wise. The results of classification with the two classes as urban and non urban indicate that the SVM outperformed the Wishart supervised classification algorithm.
{"title":"Urban area classification with quad-pol L-band ALOS-2 SAR data: A case of Chennai city, India","authors":"Dhanashri S. Kanade, V. S. K. Vanama, S. Shitole","doi":"10.1109/InGARSS48198.2020.9358951","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358951","url":null,"abstract":"Globally, 55% of the population lives in urban areas in 2018, and this number is expected to hit 68% by 2050. Earth Observation (EO) images based mapping of the urban regions is a critical parameter in the sustainable urban planning process. In recent years, rapid urban growth is experienced in the coastal metropolitan city of India-Chennai. The two land regions, having heterogeneous land uses, as high-rise high-density and medium-rise low-density of the Chennai city are taken as study area. The fully-polarimetric L-band ALOS-2 Synthetic Aperture Radar (SAR) data is used for rapid identification of the urban regions. With respect to this, a comparative assessment of the two supervised classification algorithms such as Wishart and Support Vector Machine (SVM) is presented. The same training data set is used for both algorithms, and a confusion matrix is created algorithm wise. The results of classification with the two classes as urban and non urban indicate that the SVM outperformed the Wishart supervised classification algorithm.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"70 1","pages":"58-61"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79956901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358941
S. Manikandan, Chhabi Nigam, S. Ramakrishnan, D. Seshagiri
VideoSAR is the latest technology, where the radar system enables continuous data collection and processes SAR imagery as a sequence of images continuously when the radar platform either flies by or circles the region of interest. In this paper, the Synthetic Aperture Radar video is generated from Strip map mode and Spot light mode of SAR images. In case of strip map images to video conversion, the continuous image sequence is formed by mosaicing of multiple image strips and or by overlapping of batches. In spot mode, the target of interest is fixed and platform with radar flies by or in circular path and looks at same region of interest. Two to five spot images are considered per second to form the Spot video for 24 frames per second. Interpolation techniques are performed in between the spot images to make it into 24 frames. The results of the airborne Strip map and Spot mode SAR videos are given in the paper. The frame rates also analyzed for the airborne radar of the different velocities, ranges and resolution of images to produce better SAR video.
{"title":"Generation of Airborne Synthetic Aperture Radar Video from Stripmap and Spot mode images and Frame Rate Analysis","authors":"S. Manikandan, Chhabi Nigam, S. Ramakrishnan, D. Seshagiri","doi":"10.1109/InGARSS48198.2020.9358941","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358941","url":null,"abstract":"VideoSAR is the latest technology, where the radar system enables continuous data collection and processes SAR imagery as a sequence of images continuously when the radar platform either flies by or circles the region of interest. In this paper, the Synthetic Aperture Radar video is generated from Strip map mode and Spot light mode of SAR images. In case of strip map images to video conversion, the continuous image sequence is formed by mosaicing of multiple image strips and or by overlapping of batches. In spot mode, the target of interest is fixed and platform with radar flies by or in circular path and looks at same region of interest. Two to five spot images are considered per second to form the Spot video for 24 frames per second. Interpolation techniques are performed in between the spot images to make it into 24 frames. The results of the airborne Strip map and Spot mode SAR videos are given in the paper. The frame rates also analyzed for the airborne radar of the different velocities, ranges and resolution of images to produce better SAR video.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"52 1","pages":"142-145"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85711530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358973
Y. Turkar, C. Aluckal, Y. Dighe, S. Deshpande, Y. Agarwadkar
In the recent past, precision agriculture has proven to be an effective means for farmers to optimize productions by reducing efforts and losses. Usage of UAV has proven to be beneficial for large scale agriculture. The applications of UAV in small scale agriculture and horticulture has certain limitations due to the scale and elevation variations. The current paper aims at conceptualizing a novel remote sensing-based framework for optimizing spraying locations and heights for horticulture. The data used constitutes of DEM and visual images captured from UAV platform. The paper also covers a small use-case for coconut tree plantation for implementation and validation. The results suggest that implementation of such algorithm may help in reducing wastage of spraying chemicals and in-turn will reduce adverse environmental impacts of spraying. Further integrating current work with UAV systems for optimization of path will improve UAV efficiency.
{"title":"Conceptualization of Uav Based Waypoint Generation for Precision Horticulture","authors":"Y. Turkar, C. Aluckal, Y. Dighe, S. Deshpande, Y. Agarwadkar","doi":"10.1109/InGARSS48198.2020.9358973","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358973","url":null,"abstract":"In the recent past, precision agriculture has proven to be an effective means for farmers to optimize productions by reducing efforts and losses. Usage of UAV has proven to be beneficial for large scale agriculture. The applications of UAV in small scale agriculture and horticulture has certain limitations due to the scale and elevation variations. The current paper aims at conceptualizing a novel remote sensing-based framework for optimizing spraying locations and heights for horticulture. The data used constitutes of DEM and visual images captured from UAV platform. The paper also covers a small use-case for coconut tree plantation for implementation and validation. The results suggest that implementation of such algorithm may help in reducing wastage of spraying chemicals and in-turn will reduce adverse environmental impacts of spraying. Further integrating current work with UAV systems for optimization of path will improve UAV efficiency.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"438 1","pages":"150-153"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76671674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358976
Pratikkumar A. Patel, Tejas Turakhia, Rajesh C. Iyer, A. Chhabra
In the present study, we have measured Black Carbon (BC) mass concentration over Ahmedabad city during the year 2017 and 2018. The measurements of BC have been carried out at various locations of the city during winter, summer, and post monsoon seasons. The concentration of black carbon has been found high in industrial areas and traffic junctions. In 2017, the measured high black carbon concentration was 80.73 µg/m3 and in 2018, it has increased to 83.7 µg/m3. Delta C value generally indicates wood burning as a BC source and its value is up to 20.38 µg/m3 in Ahmedabad during 2017-18. This study is helpful to estimate the major hotspots of BC mass concentration over the city and we have tried to find the major contributors to BC emission.
{"title":"Field Investigations of Black Carbon Concentration in Ambient Air Quality of a Megacity: A Case Study of Ahmedabad","authors":"Pratikkumar A. Patel, Tejas Turakhia, Rajesh C. Iyer, A. Chhabra","doi":"10.1109/InGARSS48198.2020.9358976","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358976","url":null,"abstract":"In the present study, we have measured Black Carbon (BC) mass concentration over Ahmedabad city during the year 2017 and 2018. The measurements of BC have been carried out at various locations of the city during winter, summer, and post monsoon seasons. The concentration of black carbon has been found high in industrial areas and traffic junctions. In 2017, the measured high black carbon concentration was 80.73 µg/m3 and in 2018, it has increased to 83.7 µg/m3. Delta C value generally indicates wood burning as a BC source and its value is up to 20.38 µg/m3 in Ahmedabad during 2017-18. This study is helpful to estimate the major hotspots of BC mass concentration over the city and we have tried to find the major contributors to BC emission.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"9 1","pages":"181-184"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89762394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358929
Junaid Ansari, S. Ghosh, Mukunda Dev Behera, Sharad Kumar Gupta
In this study speckle noise is removed from Sentinel-1A synthetic aperture radar (SAR) image of Sundarbans mangrove forest of West Bengal, India. Several adaptive and non-adaptive filters such as Median, Frost, Lee, Gamma maximum a posteriori (MAP) and Boxcar filter are compared for their capability in removing speckle noise. The output obtained from filtering processes are compared using visual interpretation and quantitative measures such as mean squared error, average difference, and peak signal to noise ratio, etc. The results show that boxcar filter performs better than other methods for removal of speckle noise while preserving edges of objects in the image visually.
{"title":"A Study on Speckle Removal Techniques for Sentinel-1A SAR Data Over Sundarbans, Mangrove Forest, India","authors":"Junaid Ansari, S. Ghosh, Mukunda Dev Behera, Sharad Kumar Gupta","doi":"10.1109/InGARSS48198.2020.9358929","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358929","url":null,"abstract":"In this study speckle noise is removed from Sentinel-1A synthetic aperture radar (SAR) image of Sundarbans mangrove forest of West Bengal, India. Several adaptive and non-adaptive filters such as Median, Frost, Lee, Gamma maximum a posteriori (MAP) and Boxcar filter are compared for their capability in removing speckle noise. The output obtained from filtering processes are compared using visual interpretation and quantitative measures such as mean squared error, average difference, and peak signal to noise ratio, etc. The results show that boxcar filter performs better than other methods for removal of speckle noise while preserving edges of objects in the image visually.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"15 1","pages":"90-93"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90434052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358957
H. Tulapurkar, Biplab Banerjee, B. Mohan
Convolutional neural networks (CNN) which is a feature-based machine learning algorithm is very popular in hyperspectral image (HSI) classification. CNN exploits the spatial relationship between HIS. However, HSI intrinsically have a sequence-based data structure called the spectral features. Combining spectral and spatial information offers a more comprehensive classification approach. 3D-CNN can exploit Spatial-spectral relationship but can be computationally expensive. LSTM, an important branch of the deep learning family, is mainly designed to handle Sequential data. In this paper we propose a model that uses the 1D CNN and 2D-CNN for extracting the spatial features and a LSTM for extracting the spectral features. Experimental results show that our method outperforms the accuracies reported in the existing CNN and LSTM based methods.
{"title":"Effective and Efficient Dimensionality Reduction of Hyperspectral Image using CNN and LSTM network","authors":"H. Tulapurkar, Biplab Banerjee, B. Mohan","doi":"10.1109/InGARSS48198.2020.9358957","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358957","url":null,"abstract":"Convolutional neural networks (CNN) which is a feature-based machine learning algorithm is very popular in hyperspectral image (HSI) classification. CNN exploits the spatial relationship between HIS. However, HSI intrinsically have a sequence-based data structure called the spectral features. Combining spectral and spatial information offers a more comprehensive classification approach. 3D-CNN can exploit Spatial-spectral relationship but can be computationally expensive. LSTM, an important branch of the deep learning family, is mainly designed to handle Sequential data. In this paper we propose a model that uses the 1D CNN and 2D-CNN for extracting the spatial features and a LSTM for extracting the spectral features. Experimental results show that our method outperforms the accuracies reported in the existing CNN and LSTM based methods.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"114 1","pages":"213-216"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77582976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358917
Shoba Periasamy, K. Ravi
The imaginary part of dielectric constant (ε") was retrieved from SAR of C-band (5.36 GHz) frequency for three different moisture conditions, 25%, 50%, and 70%, using a semi-empirical model. The study has found that with 25% moisture content, the accuracy level of ε" has been considerably diluted (R2=0.702, RMSE=2.475, Bias=-2.244). The influence of roughness was observed to be higher in this particular condition. With 50% moisture content, the retrieval of ε" was significantly influenced by soil textural variations (R2=0.836, RMSE=1, Bias=-0.556) but not by surface periodicity. The simulation showed promising results (R2=0.885, RMSE=0.769, Bias=0.129) in saturated condition irrespective of the soil’s textural and roughness characteristics. The study has demonstrated that the C-band SAR is more significant in explaining ε" in a saturated state.
{"title":"The Effect of Varying Moisture Content in the Retrieval of the Imaginary Part of Dielectric Constant from C-Band Frequency SAR","authors":"Shoba Periasamy, K. Ravi","doi":"10.1109/InGARSS48198.2020.9358917","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358917","url":null,"abstract":"The imaginary part of dielectric constant (ε\") was retrieved from SAR of C-band (5.36 GHz) frequency for three different moisture conditions, 25%, 50%, and 70%, using a semi-empirical model. The study has found that with 25% moisture content, the accuracy level of ε\" has been considerably diluted (R2=0.702, RMSE=2.475, Bias=-2.244). The influence of roughness was observed to be higher in this particular condition. With 50% moisture content, the retrieval of ε\" was significantly influenced by soil textural variations (R2=0.836, RMSE=1, Bias=-0.556) but not by surface periodicity. The simulation showed promising results (R2=0.885, RMSE=0.769, Bias=0.129) in saturated condition irrespective of the soil’s textural and roughness characteristics. The study has demonstrated that the C-band SAR is more significant in explaining ε\" in a saturated state.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"47 1","pages":"114-117"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76496868","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358965
G. Tripathi, Arvind Chandra Pandey, Bikash Ranjan Parida
Ganga River’s water quality has been improved during COVID-19 lockdowns in India (24th March to 18th May, 2020) while comparing with the normal days. This study attempted to highlight the variation in river’s water quality in terms of spatio temporal turbidity. This study is based on the analysis of remote sensing. Red band known as the most sensitive to estimate turbidity. The temporal variation in turbidity was also investigated through linear regression model using Sentinel-2A, B optical satellite data. It was observed that before lockdown period as on 3rd March 2020, mean turbidity was estimated as 13.47 FTU (Formazine Turbidity Unit) and during as on 2nd April 2020, estimated as 11.74 FTU. Further, on 17th April 2020, it was increased with 0.25 and estimated as 11.99 FTU. Hence, it can be concluded that due to less anthropogenic activity led by the lockdown imposed in the country, water quality of the river is improving continuously. The study also exhibited the relevancy of remote sensing approach to make qualitative estimates on turbidity, when there are no field observations.
{"title":"Spatio- Temporal Analysis of Turbidity in Ganga River in Patna, Bihar Using Sentinel-2 Satellite Data Linked with Covid-19 Pandemic","authors":"G. Tripathi, Arvind Chandra Pandey, Bikash Ranjan Parida","doi":"10.1109/InGARSS48198.2020.9358965","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358965","url":null,"abstract":"Ganga River’s water quality has been improved during COVID-19 lockdowns in India (24th March to 18th May, 2020) while comparing with the normal days. This study attempted to highlight the variation in river’s water quality in terms of spatio temporal turbidity. This study is based on the analysis of remote sensing. Red band known as the most sensitive to estimate turbidity. The temporal variation in turbidity was also investigated through linear regression model using Sentinel-2A, B optical satellite data. It was observed that before lockdown period as on 3rd March 2020, mean turbidity was estimated as 13.47 FTU (Formazine Turbidity Unit) and during as on 2nd April 2020, estimated as 11.74 FTU. Further, on 17th April 2020, it was increased with 0.25 and estimated as 11.99 FTU. Hence, it can be concluded that due to less anthropogenic activity led by the lockdown imposed in the country, water quality of the river is improving continuously. The study also exhibited the relevancy of remote sensing approach to make qualitative estimates on turbidity, when there are no field observations.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"1 1","pages":"29-32"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89742830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-01DOI: 10.1109/InGARSS48198.2020.9358959
P. Jayasri, K. Niharika, S. Priya, C. V. Ramana Sarma, H. U. Sundari, E. S. Sita Kumari
This paper summarizes the radiometric and geometric calibration results achieved during commissioning and operational phase of RISAT-1. To ascertain long term stability, all the imaging modes pertaining to Stripmap, ScanSAR and High resolution Spotlight light modes were calibrated and validated using data collected over ISRO Cal sites and homogenous distributed targets. The characterization of RISAT-1 SAR has been performed by deriving the Elevation Antenna Patterns using gamma naught analysis, distributed target analysis, image quality metrics of the data products, estimation of calibration factor along with RCS characterization of Corner reflectors. The performance evaluation of nominal Medium Resolution ScanSAR mode having 25days repetivity of India’s carpet coverage was carried out periodically. Nonetheless scattering mechanisms pertaining to Hybrid polarimetry were also studied against the response of corner reflectors. The experience gained in carrying out SAR calibration activity to perform absolute and relative calibration of RISAT-1 and their results are described in this paper.
{"title":"RISAT-1 SAR External Calibration – A Summary","authors":"P. Jayasri, K. Niharika, S. Priya, C. V. Ramana Sarma, H. U. Sundari, E. S. Sita Kumari","doi":"10.1109/InGARSS48198.2020.9358959","DOIUrl":"https://doi.org/10.1109/InGARSS48198.2020.9358959","url":null,"abstract":"This paper summarizes the radiometric and geometric calibration results achieved during commissioning and operational phase of RISAT-1. To ascertain long term stability, all the imaging modes pertaining to Stripmap, ScanSAR and High resolution Spotlight light modes were calibrated and validated using data collected over ISRO Cal sites and homogenous distributed targets. The characterization of RISAT-1 SAR has been performed by deriving the Elevation Antenna Patterns using gamma naught analysis, distributed target analysis, image quality metrics of the data products, estimation of calibration factor along with RCS characterization of Corner reflectors. The performance evaluation of nominal Medium Resolution ScanSAR mode having 25days repetivity of India’s carpet coverage was carried out periodically. Nonetheless scattering mechanisms pertaining to Hybrid polarimetry were also studied against the response of corner reflectors. The experience gained in carrying out SAR calibration activity to perform absolute and relative calibration of RISAT-1 and their results are described in this paper.","PeriodicalId":6797,"journal":{"name":"2020 IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","volume":"76 1","pages":"82-85"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86308263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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