Faculty Publications
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Publications by NITK Faculty
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Item YOLOv5 Model-based Ship Detection in High Resolution SAR Images(Institute of Electrical and Electronics Engineers Inc., 2023) Sapna, S.; Sandhya, S.; Shetty, R.D.; Pais, S.M.; Bhattacharjee, S.Detection of ships in Synthetic Aperture Radar (SAR) images play a crucial role in maritime surveillance, most importantly under complex sea conditions. SAR permits observation in any weather conditions, at all hours of the day and night. At present, the ship detection from SAR images is a notable area of research since it is very difficult to detect the ships in the SAR images using traditional object or target detection algorithms. In this work, a You Only Look Once version 5 (YOLOv5) based ship detection model from SAR images with faster training speed and higher accuracy is implemented and tested. This model achieved a mean average precision (mAP) of 96.2% with a training time of 8.63 hours. This work also provides a comparative analysis with the existing methods for detection of ships in SAR images. The comparison shows that the YOLOv5 based model performs better in terms of both mean average precision and training time when compared to the existing models. © 2023 IEEE.Item Prediction of High-Resolution Atmospheric CO2 Concentration from OCO-2 using Machine Learning(Association for Computing Machinery, 2023) Pais, S.M.; Bhattacharjee, S.; Anand Kumar, A.K.Carbon Dioxide (CO2) is a greenhouse gas (GHG) emitted by human anthropogenic activities. The satellite measurement of atmospheric column-averaged CO2 concentration (XCO2) provides an excellent opportunity to understand the global carbon cycle for a large comprehensive temporal range. Orbiting Carbon Observatory-2 (OCO-2) satellite provides highly accurate data with a spatial resolution of approximately 3 km2. However, OCO-2 measures one location on the Earth's surface almost fortnightly. Also, the clouds and aerosols cause missing data. In this work, the OCO-2 measurements, along with Open-Source Data Inventory for Anthropogenic CO2 (ODIAC) emission estimate, are considered. A spatial upscaling, followed by different machine learning methods are used to predict high-resolution, continuous mapping of XCO2. The prediction models are evaluated using the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE) for Germany, considering a temporal range of November 2018 to December 2019. The least error is attained by monthly model, achieving a MAE and RMSE of 0.707 ppm and 1.187 ppm, respectively, using the extremely randomized trees (ERT) method. The predictions are externally validated using Total Carbon Column Observing Network (TCCON) ground-based measurements as well. © 2023 ACM.Item Disaster Classification Using Multimodality Techniques by Integrating Images and Text(Springer Science and Business Media Deutschland GmbH, 2025) Medapati, B.M.R.; Pais, S.M.; Bhattacharjee, S.In today’s digital era, the abundance of information shared through social media platforms in times of disaster has emerged as a crucial asset for enhancing disaster response operations. This research initiative was specifically dedicated to enhance disaster categorization by integrating image and tweet text data. The devised model comprises two distinct modules aimed at optimizing the classification process. The primary module focuses on extracting insights from text and images independently using VGG-16 for images, and Bidirectional Long Short-Term Memory and Convolutional Neural Network for texts, subsequently executing the classification task. Conversely, the secondary module is designed to learn the interconnectedness between textual content and images using Contrastive Learning Image Pretraining (CLIP). After this late fusion is used to combine the outcomes of these modules and later softmax classification is used for the classification of the incident into one of seven humanitarian categories thereby enhancing the precision and efficacy of disaster classification. The developed model gives an accuracy of 75% with no data and image augmentations and the result was improved to 93% with different combinations of augmentations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.Item Predicting High-Resolution Soil Moisture Using MODIS Bands: A Fusion-Regression Approach(Institute of Electrical and Electronics Engineers Inc., 2025) Sudhakara, B.; Pais, S.M.; Bhattacharjee, S.Soil moisture (SM) is a crucial biophysical parameter that plays a significant role in predicting food security. Monitoring SM is essential as it provides valuable insights into agricultural productivity and the impacts of climate change. Remote sensing platforms retrieve SM data from various satellites, enabling global monitoring. However, the coarse resolution of these datasets limits their ability to capture fine-scale variations in SM. To address this challenge, this study aims to generate a high-resolution SM product using data from the Soil Moisture Active Passive (SMAP) mission. The SMAP data is fused with MODIS spectral bands to create a multi-source dataset, which is then used as input for different regression models to achieve downscaling of SM. Experimental results demonstrate that the Extremely Randomized Trees (ERT) model achieves the minimum error, with RMSE 0.0113 m3/m3, MAE 0.0172 m3/m3, and R2 0.9725. The study focuses on Karnataka, covering the temporal window from 2015 to 2022. © 2025 IEEE.Item Downscaled XCO2 Estimation Using Data Fusion and AI-Based Spatio-Temporal Models(Institute of Electrical and Electronics Engineers Inc., 2024) Pais, S.M.; Bhattacharjee, S.; Anand Kumar, M.; Chen, J.One of the well-known greenhouse gases (GHGs) produced by anthropogenic human activity is carbon dioxide (CO2). Understanding the carbon cycle and how negatively it affects the ecosystem requires analysis of the rise in CO2 concentration. This work aims to map CO2 concentration for the entire surface, making it useful for regional carbon cycle analysis. Here, column-averaged CO2 dry mole fraction, called XCO2, measured by the orbiting carbon observatory-2 (OCO-2) satellite, is used. Because of spectral interference by the clouds and aerosols, there are many missing footprints in the Level-2 swath of OCO-2, making it disruptive to understand any assessment related to the carbon cycle. The objective of this work is to predict 1 km2 XCO2 using data resampling and machine learning models. This work achieves a minimum mean absolute error (MAE) and root mean square error (RMSE) of 0.3990 and 0.8090 ppm, using the monthly models. © 2004-2012 IEEE.Item Forecasting of Fine-Grained SIF of OCO-2 Using Multi-source Data and AI-Based Techniques(Springer, 2025) Pais, S.M.; Bhattacharjee, S.; Anand Kumar, M.The emission of unused energy absorbed from the sunlight by plants and other photosynthetic organisms is known as solar-induced fluorescence (SIF). SIF is a direct proxy for the photosynthetic activity of the plants used to monitor drought, crop yield estimation, ecological processes, and carbon cycles. Comprehending the SIF dynamics beforehand helps gain an understanding of vegetation dynamics, carbon cycle, and crop phenology. This study explores the potential of using Orbiting Carbon Observatory-2 (OCO-2) SIF data for forecasting SIF at regional scales. The research utilizes machine learning models and data fusion to forecast the SIF data, by establishing relationships between observed SIF from past timestamps and the Enhanced Vegetation Index. The lasso regression achieves minimal error of RMSE 0.0355 Wm-2nm-1sr-1 and MAPE of 16.9093% for forecasting monthly SIF data. In contrast, the light gradient boosting machine model (LG) performs well for a larger non-linear dataset, i.e., seasonal models achieving a RMSE of 0.0389 Wm-2nm-1sr-1 and MAPE of 17.4895%, respectively. Karnataka and Maharastra, the two Indian states, are considered as the study areas for this work for a temporal window of 2017–2019. Fine-grained, uniformly distributed SIF forecasting provides valuable insights for understanding vegetation responses to environmental changes, optimizing agricultural practices, and developing climate change mitigation strategies. © Indian Society of Remote Sensing 2025.Item High-Spatial-Resolution Estimation of XCO2 Using a Stacked Ensemble Model(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Pais, S.M.; Bhattacharjee, S.; Anand Kumar, M.; Balamurugan, V.; Chen, J.Highlights: What are the main findings? The study develops a customized stacked ensemble model that generalizes (Formula presented.) predictions across multiple country, such as Germany, France, and Japan. It produces gap-filled high-resolution monthly, seasonal, and yearly maps, highlighting vegetation dynamics and seasonal cycles. What is the implication of the main finding? The customized stacked ensemble model provides reliable cross-country (Formula presented.) predictions at 1 (Formula presented.) resolution, validated against TCCON and CAMS, supporting large-scale environmental monitoring. Seasonal and yearly analyses show vegetation dynamics and photosynthetic activity significantly influence (Formula presented.), enhancing the model’s adaptability for agriculture, different climate assessments, and future global mapping. One of the leading causes of climate change and global warming is the rise in carbon dioxide ((Formula presented.)) levels. For a precise assessment of (Formula presented.) ’s impact on the climate and the creation of successful mitigation methods, it is essential to comprehend its distribution by analyzing (Formula presented.) sources and sinks, which is a challenging task using sparsely available ground monitoring stations and airborne platforms. Therefore, the data retrieved by the Orbiting Carbon Observatory-2 (OCO-2) satellite can be useful due to its extensive spatial and temporal coverage. Sparse and missed retrievals in the satellite make it challenging to perform a thorough analysis. This work trains machine learning models using the Orbiting Carbon Observatory-2 (OCO-2) (Formula presented.) retrievals and auxiliary features to obtain a monthly, high-spatial-resolution, gap-filled (Formula presented.) concentration distribution. It uses a multi-source aggregated (MSD) dataset and the generalized stacked ensemble model to predict country-level high-resolution (1 (Formula presented.)) (Formula presented.). When evaluated with TCCON, this country-level model can achieve an RMSE of 1.42 ppm, a MAE of 0.84 ppm, and (Formula presented.) of 0.90. © 2025 by the authors.