Faculty Publications
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Publications by NITK Faculty
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Item Comparison of Neural Networks for Binary Spatial Classification of Rice Field by Studying Temporal Pattern using Dual Polarimetric SAR Measurements(Springer, 2024) Aishwarya Hegde, A.; Umesh, P.; Tahiliani, M.P.Timely and precise information on rice cultivation plays a pivotal role in reshaping the global food and agricultural system. Synthetic aperture radar, with its capability to observe around the clock and in all weather conditions, is an invaluable tool for monitoring rice distribution. Such comprehensive cropland data at vast spatial scales not only enhances crop management but also provides critical support to governmental decision-making processes. The paper focuses on Binary classification by learning the temporal pattern of the Rice pixel. Time series curves of VV, VH, VV+VH, and VV/VH polarization and major rice varieties, MO4 and Kaje Jaya, cultivated in the area are analyzed to study the similarity of the curves the similarities in the curves, which could influence the temporal pattern recognition capacity of deep learning models. The study underscores the superior performance of RNN models, particularly BiLSTM and the proposed Dual Branch BiLSTM, over their CNN counterparts, such as 3DCNN and 3DUNET, especially for the VH and VV+VH polarizations. Specifically, the Dual Branch BiLSTM emerged as a standout, exhibiting an accuracy rate of 99.92% for combination of VH and VV+VH polarization. This model adeptly combined features from both VH and VV+VH polarizations, ensuring robust rice field mapping. Our results present a promising avenue for enhanced rice mapping, especially in tropical or subtropical zones, through the nuanced application of deep learning models. © Indian Society of Remote Sensing 2024.Item Multi-season rice mapping using deep learning models with multitemporal Sentinel-1 SAR data in the Kuttanad Delta, Kerala(Taylor and Francis Ltd., 2025) Aishwarya Hegde, A.; Nair, M.K.; Umesh, P.; Tahiliani, M.P.Timely and precise monitoring of rice paddies is essential for sustaining production, ensuring food security, and addressing climate challenges, as rice is a significant contributor to greenhouse gas emissions. Accurate rice mapping, facilitated by Sentinel-1 SAR, unaffected by weather is used in Machine learning (ML) and Deep learning (DL) models for multiclass classification of rice cropping seasons by analysing temporal backscatter patterns. A modified Dual-Branch BiLSTM model is developed to capture VH backscatter variations across the homogeneous and heterogeneous rice-growing landscapes. The study compares the performance of ML models, Random Forest (RF) and Support Vector Machine (SVM), with DL models, BiGRU and BiLSTM-BiGRU, for mapping Rabi, Kharif, double-cropping rice fields, and non-rice areas in the Kuttanad Delta region. A thorough evaluation of the proposed models was conducted using metrics like Precision, Recall, and F1 Score to assess their effectiveness. The results show that the Modified Dual-branch BiLSTM model attains F1 scores as high as 0.97 in homogeneous regions and 0.94 in heterogeneous rice-growing landscapes, highlighting its robustness and strong generalisation in mapping rice in varied landscape areas, particularly in the cloudy tropical and subtropical regions where optical data are often not consistently available during the rice cultivation season. © 2025 Informa UK Limited, trading as Taylor & Francis Group.