Fuzzy Contrast Improvement for Low Altitude Aerial Images

Precision agriculture is becoming very important in improving food security. Unmanned Aerial Vehicles (UAVs) have higher possibilities in this way, improving real time data gathered with aerial sensors. Fuzzy techniques have proved to be highly effective in managing vagueness and ambiguity. The unmanned helicopters are highly valuable due to the level of maneuverability that they possess. We believe that many different degrees of autonomy and functionalities of UAVs will be useful in agriculture. We present a new process to extract data from aerial images that comes from low altitude UAVs. We combined NDVI algorithm output with the RSWHE-M method on grey scaled images. Primary results show that our method extracts images that are visually acceptable to human eye and have a natural appearance.