2.6.2. Maximum Likelihood Classification (MLC)

MLC is a nonlinear discriminant function based on the Bayesian rule that is suitable for processing low-dimensional data [42]. MLC assumes that a hyper ellipsoid decision volume can be applied to approximate the shapes of the data clusters. The mean vector and covariance matrix of specified unknown pixels in each class is calculated. The probability of each pixel's class is then analyzed, and the pixel is predicted to belong to the class with the maximum probability [43]. In this study, the MLC module from the ENVI software was used to identify maize lodging. The data scale factor, which is a major parameter in the classifier, was set to 1.0 in each classification. This is mainly because the UAV images and their characteristics involved in this study are floating-point data. The UAV image features and ROI regions were provided as the input data for the MLC to generate the maize lodging recognition results.
