IEEE InGARSS 2023: Research Poster Session on Machine Learning Applications (Session 13)

This video includes presentations of research papers accepted as posters on the topic of "Machine Learning Applications" at the 2023 IEEE India Geoscience and Remote Sensing Symposium (InGARSS 2023) on December 12, 2023, at IIIT Bangalore (IIIT-B).
Research (Poster) - Session 13: ML Apps
The speakers Touseef Ahmad, Anand S Sahadevan, Soumyendu Raha, Rosly B Lyngdoh, and Deepak Putrevu offered a comprehensive examination on the topic "Superpixel Guided Weighted Low-Rank Representation for Nonlinear Hyperspectral Unmixing".
The speakers Rohit Singh, Mantosh Biswas, and Mahesh Pal offered a comprehensive examination on the topic "An Automated Cloud Detection Method for Sentinel-2 Imageries".
The speakers Jayantrao Mohite, Suryakant Sawant, Ankur Pandit, Jay Prakash Kumar, and Srinivasu Pappula offered a comprehensive examination on the topic "Sugarcane Area Mapping with Zero Labels Using Hybrid Unsupervised and Supervised Approach".
The speakers Geetika Barman and B.S. Daya Sagar offered a comprehensive examination on the topic "A New Approach to Compute Vector Based Morphological Features for Classification of Hyperspectral Image".
The speakers Soorya Suresh and Arun P V offered a comprehensive examination on the topic "Unmixing in Latent Space: A Novel Unsupervised Approach for Geological Mapping of Lunar Surface".
The speakers Yash Mittal, Uttam Kumar, and Deeksha Aggarwal offered a comprehensive examination on the topic "Online Incremental Machine Learning Model for Multi-Sensor and Multi-Temporal Satellite Data: A Case Study of Bangalore".
The speakers Amol Patil and Kedar Kulkarni offered a comprehensive examination on the topic "A Hybrid Machine Learning - Numerical Weather Prediction Approach for Rainfall Prediction".
The speakers Satya Prakash, Jayan Wijesingha, Sunil Nautiyal, and Michael Wachendorf offered a comprehensive examination on the topic "A Systematic Procedure for Land Use Land Cover Mapping with Remote Sensing Data and Machine Learning".
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