Agricultural Field Detection from Satellite Imagery Using the Combined Otsu’s Thresholding Algorithm and Marker-Controlled Watershed-Based Transform

An accurate detection of agricultural fields is often needed for agricultural-related applications, such as subsidies monitoring, field-based crop yield estimation and agricultural statistics extraction. High-resolution space images have become the fundamental source to extract agricultural field boundaries. Manual boundary delineation is not practical. In this study, we present an approach to detect agricultural fields from satellite images on the basis of agricultural field blocks. An agricultural field block consists of one or more fields that are owned by the farmers. The approach combines the Otsu’s thresholding algorithm and marker-controlled watershed (MCW)-based segmentation. First, the well-separated field segments within a field block being considered are detected through recursive processing of the Otsu’s thresholding algorithm. Then, these distinct field segments are used to generate a marker image, and further extraction of individual fields is carried out through a marker-controlled watershed (MCW)-based segmentation. The approach was tested using 10-m resolution Satellite Pour l’Observation de la Terre (SPOT)-5 multi-spectral (XS) image, 4-m resolution IKONOS XS image, 2.40-m resolution QuickBird XS image, and 0.60-m resolution QuickBird pan-sharpened (PS) image. The results were evaluated using the reference field boundary dataset. The achieved overall accuracies were 89.7, 83.2, 81.0, and 77.4% for the IKONOS XS, QuickBird XS, SPOT-5 XS, and QuickBird PS images, respectively. The results are promising and indicate that the approach can be used for the extraction of agricultural fields from space imagery.