Crop Separability from Individual and Combined Airborne Imaging Spectroscopy and UAV Multispectral Data

Abstract

Crop species separation is essential for a wide range of agricultural applications—in particular, when seasonal information is needed. In general, remote sensing can provide such information with high accuracy, but in small structured agricultural areas, very high spatial resolution data (VHR) are required. We present a study involving spectral and textural features derived from near-infrared (NIR) Red Green Blue (NIR-RGB) band datasets, acquired using an unmanned aerial vehicle (UAV), and an imaging spectroscopy (IS) dataset acquired by the Airborne Prism EXperiment (APEX). Both the single usage and combination of these datasets were analyzed using a random forest-based method for crop separability. In addition, different band reduction methods based on feature factor loading were analyzed. The most accurate crop separation results were achieved using both the IS dataset and the two combined datasets with an average accuracy (AA) of >92%. In addition, we conclude that, in the case of a reduced number of IS features (i.e., wavelengths), the accuracy can be compensated by using additional NIR-RGB texture features (AA > 90%).

Keywords

• crop separability
• imaging spectroscopy
• multispectral drone data
• random forest
• band reduction