Crop Classification using Semi supervised Learning on Data Fusion of SAR and Optical Sensor

Abstract

Crop maps are essential tools for creating crop inventories, forecasting yields, and guiding the use of efficient farm management techniques. These maps must be created at highly exact scales, necessitating difficult, costly, and time-consuming fieldwork. Deep learning algorithms have now significantly enhanced outcomes when using data in the geographical and temporal dimensions, which are essential for agricultural research. The simultaneous availability of Sentinel-1 (synthetic aperture radar) and Sentinel-2 (optical) data provides an excellent chance to combine them. Sentinel 1 and Sentinel 2 data sets were collected for the Cape Town, South Africa, region. With the use of these datasets, we use the fusion technique, particularly the layer-level fusion strategy, one of the three fusion procedures (input level, layer level, and deci-sion level). Also, we will compare the results before and after the fusion and discuss the recommended method for converting from a multilayer perceptron decoder to a semi-supervised decoder architecture.