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3rd IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2023

作者： Misra, Indranil Sinha, Abhishek Kumar Shukla, Tushar Arya, Sunita Sharma, Vivek Gaur, Neha Moorthi, S Manthira Dhar, Debajyoti ISRO Signal and Image Processing Area Space Applications Centre Ahmedabad India

ISBN: (纸本)9798350325591

The India-Bhutan SAT (INS-2B) satellite was launched on 26th November 2022 from the Indian Spaceport Sriharikota. The Nano-MX instrument onboard the INS-2B satellite provides multispectral remote sensing images over the Indian subcontinent. The primary objective of Nano-MX is to map Bhutan for natural resource monitoring, including forest assessment, wetland monitoring, and snow/glacier studies. The paper presents insight into the radiometric and geometric performance of Nano-MX using in-orbit sensor data. The radiometric correction procedures adopted for Nano-MX raw images are described, SNR performance and MTF measurement from Nano-MX images are computed. The geometric performance includes Band-to-Band Registration (BBR) characterization and improved location accuracy of Nano-MX data products estimated at diverse terrains. The reflectance profile generated from Nano-MX images is plotted for spectral bands, and Nano-MX surface reflectance is cross-validated with Landsat-9 OLI. In addition, Bhutan country-level mosaic using Nano-MX images confirms spatial/spectral fidelity across large geographical regions. The initial data processing results show that INS-2B Nano-MX radiometric and geometric quality parameters meet the required sensor specifications. © 2023 IEEE.

关键词： Remote sensing

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Reconstruction of Missing Multiband images for High Resolution Multispectral Sensors Using Wasserstein GAN 3

Reconstruction of Missing Multiband Images for High Resoluti...

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3rd IEEE India Geoscience and Remote Sensing Symposium, InGARSS 2023

作者： Hossain, Md Aminur Gupta, Ashutosh Paul, Subhajit Singh, Sanjay K. Naidu, S. Devakanth Dhar, Debajyoti Signal And Image Processing Area Space Applications Centre ISRO Ahmedabad India

ISBN: (纸本)9798350325591

Today's Multispectral (MX) imaging systems contain multiple bands with unprecedented high spatial resolution and swath. Due to the complex mechanisms involved in image acquisition and data transmission for such systems, the information in all or a subset of these bands may be lost, rendering the dataset unfit for end-use. In such scenarios, it is imperative to design methods that can faithfully reconstruct the data given the available subset of bands or a simultaneously acquired Panchromatic image (PAN). In this paper, we propose a method to reconstruct lost multiband regions in MX images using a Wasserstein Generative Adversarial Network (WGAN) with expert regularization. Specifically, we demonstrate the strength of our network by synthesizing RGB (Red, Green, Blue) bands from co-registered Near-InfraRed (NIR) and PAN images as input. Qualitative and quantitative results obtained for experiments performed on Cartosat-MX images show that our method is able to reconstruct images that are spatially and spectrally accurate. © 2023 IEEE.

关键词： Generative adversarial networks

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Towards Adversarial Robustness and Reducing Uncertainty Bias through Expert Regularized Pseudo-Bidirectional Alignment in Transductive Zero Shot Learning 27th

Towards Adversarial Robustness and Reducing Uncertainty Bias...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Kumar Sinha, Abhishek Mishra, Deepak Moorthi, S. Manthira Signal and Image Processing Area Space Applications Center Ahmedabad India Indian Institute of Space Science and Technology Thiruvananthapuram India

ISBN: (纸本)9783031781827

Transductive zero-shot learning (TZSL) aims to minimize the domain shift between the learned and true distribution of the unseen classes by allowing access to the unpaired samples from unseen classes. While many distribution alignment based methods attempt to align both visual and semantic spaces to train the classifier, their performance is still limited by confirmation bias. Additionally, bidirectional alignment approaches are based on the strong assumption that the intrinsic dimensions of visual and semantic spaces are the same, which is rarely true. In this work, we first highlight the limitations of bidirectional alignment in terms of intrinsic dimensionality. We then present a pseudo-bidirectional approach that, without any underlying assumptions on these spaces, utilizes the learned visual-to-attribute mapping to minimize the distribution shift between learned and true unseen visual feature distributions. We further utilize an entangled loss between semantic and visual space to minimize the confirmation or uncertainty bias and improve the adversarial robustness. We, theoretically and empirically, show the performance gain in addition to the adversarial robustness under the proposed setting. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Indigenous IoT and Software Framework for Implementing Digital Twin Over Indian Region Towards Sustainable Development 1

Indigenous IoT and Software Framework for Implementing Digit...

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1st International Conference on Computing, Sciences and Communications, ICCSC 2024

作者： Agarwal, Nikunj Singla, JaiG. Dhaka, Vijaypal Singh School of Computing and Intelligent Systems Manipal University Jaipur Rajasthan Jaipur India Space Applications Centre ISRO Signal and Image Processing Area Gujarat Ahmedabad India

ISBN: (纸本)9798350353648

Digital Twin is a digital replication of physical things. This technology has been in works for over a decade, and has been implemented in various regions. This study shows the integration of Internet of Things(IoT) with the Digital Twin technology seamlessly within the Indian ecosystem to lead towards sustainable development. The Digital Twin model utilizes the data collected through an indigenous IoT solution equipped with LoRa technology and Node MCU. However, with the diverse geographic properties of the Indian subcontinent, it poses a significant challenge to collect comprehensive data of the entirety of the region. This paper presents an approach to develop an indigenous and integrated solution to simplify the complexities of integrating data from the sensors spread across the region into one simple framework. This paper further present show this Digital Twin model can play a huge role in showcasing data received from indigenous IoT devices to nroduce more accurate and actionable insishts. © 2024 IEEE.

关键词： Sustainable city

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Automatic Geometric Quality Assessment Framework For Geo-Imaging Platform Data Products

Automatic Geometric Quality Assessment Framework For Geo-Ima...

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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Shailendra Kumar Joshi Shilpa Prakash Nilima Rani Chaube Debajyoti Dhar Signal and Image Processing Area Space Applications Centre (ISRO) Ahmedabad India

ISBN: (数字)9798350390346

ISBN: (纸本)9798350390353

Indian National Satellite System (INSAT), is a constellation of geostationary satellites equipped with advanced imager and Sounder payloads for enhanced meteorological observations and monitoring of Indian subcontinent region. The stability inherent in geostationary orbits is critical for generating accurate geo-coded data products, essential for weather forecasting and disaster management. Geometric quality control measures are pivotal in validating Level-1 data products. A framework has been developed to automatically assess geometric location errors using global mosaic reference images. The methodology in framework employs feature point matching with image descriptors, followed by outlier rejection and estimation of errors along the East-West and North-South directions. These error provides feedback to the data processing workflow to enhance the overall quality of the data products.

关键词： Accuracy Weather forecasting Manuals Quality control Real-time systems Orbits Quality assessment Monitoring Payloads Geostationary satellites

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Enhancing image Resolution: A Simulation Study and Sensitivity Analysis of System Parameters for Resourcesat-3S/3SA

arXiv

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arXiv 2024年

作者： Garg, Ankur Sarkar, Meenakshi Moorthi, S.M. Dhar, Debajyoti Optical Data Processing Group Signal and Image Processing Area Space Applications Centre Ahmedabad India

Resourcesat-3S/3SA, an upcoming Indian satellite, is designed with Aft andFore payloads capturing stereo images at look angles of -5deg and 26deg,respectively. Operating at 632.6 km altitude, it features a panchromatic (PAN)band offering a Ground Sampling Distance (GSD) of 1.25 meters and a 60 kmswath. To balance swath width and resolution, an Instantaneous Geometric Fieldof View (IGFOV) of 2.5 meters is maintained while ensuring a 1.25-meter GSDboth along and across track. Along-track sampling is achieved through precisetiming, while across-track accuracy is ensured by using two staggered pixelarrays. signal-to-Noise Ratio (SNR) is enhanced through Time Delay andIntegration (TDI), employing two five-stage subarrays spaced 80 {\\mu}m apartalong the track, with a 4 {\\mu}m (0.5 pixel) stagger in the across-trackdirection to achieve 1.25-meter resolution. To further boost resolution, thesatellite employs super-resolution (SR), combining multiple low-resolutioncaptures using sub-pixel shifts to produce high-resolution images. Thistechnique, effective when images contain aliased high-frequency details,reconstructs full-resolution imagery using phase information from multipleobservations, and has been successfully applied in remote sensing missions likeSPOT-5, SkySat, and DubaiSat-1. A Monte Carlo simulation explores the factorsinfluencing the resolution in Resourcesat-3S/3SA, with sensitivity analysishighlighting key impacts. The simulation methodology is broadly applicable toother remote sensing missions, optimizing SR for enhanced image clarity andresolution in future satellite systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Sensitivity analysis

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CharDiff: Improving Sampling Convergence via Characteristic Function Consistency in Diffusion Models

CharDiff: Improving Sampling Convergence via Characteristic ...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Abhishek Kumar Sinha S. Manthira Moorthi Signal and Image Processing Area Space Applications Centre (Indian Space Research Organization) Ahmedabad

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Diffusion models have demonstrated extensive capabilities for generative modelling in both conditional and conditional image synthesis tasks. Reverse sampling has been the centre of interest to improve the overall image quality without retraining the model from scratch. In this work, we propose a plug-and-play module by utilizing the charac-teristic function of the distributions to minimize sampling drift. We experiment with existing diffusion solvers with our module during the denoising step to provide additional per-formance gain in image synthesis, linear inverse problem tasks and text-conditioned image synthesis. Moreover, We theoretically establish the method's effectiveness in terms of improved Fréchet Inception Distance (FID) and second-order Tweedie moment for reduced trajectory deviation.

关键词： image quality Computer vision image synthesis Inverse problems Noise reduction Diffusion models Robustness Trajectory Convergence

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CVPR MultiEarth 2023 Deforestation Estimation Challenge: SpaceVision4Amazon

arXiv

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arXiv 2023年

作者： Arya, Sunita Moorthi, S. Manthira Dhar, Debajyoti Signal and Image Processing Area Space Applications Centre Ahmedabad India

In this paper, we present a deforestation estimation method based on attention guided UNet architecture using Electro-Optical (EO) and Synthetic Aperture Radar (SAR) satellite imagery. For optical images, Landsat-8 and for SAR imagery, Sentinel-1 data have been used to train and validate the proposed model. Due to the unavailability of temporally and spatially collocated data, individual model has been trained for each sensor. During training time Landsat-8 model achieved training and validation pixel accuracy of 93.45% and Sentinel-2 model achieved 83.87% pixel accuracy. During the test set evaluation, the model achieved pixel accuracy of 84.70% with F1-Score of 0.79 and IoU of 0.69. Copyright © 2023, The Authors. All rights reserved.

关键词： Deforestation

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Reconstruction of Missing Multiband images for High Resolution Multispectral Sensors Using Wasserstein GAN

Reconstruction of Missing Multiband Images for High Resoluti...

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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Md Aminur Hossain Ashutosh Gupta Subhajit Paul Sanjay K. Singh S. Devakanth Naidu Debajyoti Dhar Signal And Image Processing Area Space Applications Centre ISRO Ahmedabad India

Today’s Multispectral (MX) imaging systems contain multiple bands with unprecedented high spatial resolution and swath. Due to the complex mechanisms involved in image acquisition and data transmission for such systems, the information in all or a subset of these bands may be lost, rendering the dataset unfit for end-use. In such scenarios, it is imperative to design methods that can faithfully reconstruct the data given the available subset of bands or a simultaneously acquired Panchromatic image (PAN). In this paper, we propose a method to reconstruct lost multiband regions in MX images using a Wasserstein Generative Adversarial Network (WGAN) with expert regularization. Specifically, we demonstrate the strength of our network by synthesizing RGB (Red, Green, Blue) bands from co-registered Near-InfraRed (NIR) and PAN images as input. Qualitative and quantitative results obtained for experiments performed on Cartosat-MX images show that our method is able to reconstruct images that are spatially and spectrally accurate.

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INS-2B Nano-MX In-Orbit Sensor Performance and Initial Data processing Results

INS-2B Nano-MX In-Orbit Sensor Performance and Initial Data ...

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Remote Sensing Symposium (InGARSS), 2020 IEEE India Geoscience and

作者： Indranil Misra Abhishek Kumar Sinha Tushar Shukla Sunita Arya Vivek Sharma Neha Gaur S Manthira Moorthi Debajyoti Dhar Signal and Image Processing Area Space Applications Centre ISRO Ahmedabad India

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