Crop type classification using multi-sensor satellite image time series data and attention-based deep learning technique: a case study of southern India
Crop type classification is essential for effective agricultural monitoring in India. Preforming crop type classification in the diverse Indian agriculture setting is a challenging task. This thesis leverages transformer-based deep learning model, specifically the Pixel-Set Encoder Temporal Attention Encoder (PSETAE), to classify ten crop types of rabi season (2024 - 25) using Satellite Image Time
