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 dimen- sions, which are essential for agricultural research. The simultaneous avail- ability 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. The total testing accuracy produced by the Ada-Match semi-supervised decoder approach was 80.3%. We conduct studies to demonstrate that our methodology not only outperforms prior state-of-the-art approaches in terms of precision but also significantly decreases processing time and memory requirements.
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