Spatial Nonstationarity in DL-Based Crop Phenological Analysis

Vegetation index (VI) curves, derived from multitemporal satellite images, are being widely employed to model the crop-specific phenological events. The current study analyzed a novel approach to mitigate the effect of violating the independent and identically distributed (i.i.d.) assumption in classifying the VI curves. Even though deep learning (DL)-based classification methods have produced cutting-edge outcomes, the correlation of spatially adjacent samples is not generally considered. The proposed approach dynamically transformed the VI curves to a graph representation, where the nodes correspond to the curves. Graph convolutional operations along with Kolmogorov-Arnold network (KAN) were then used to learn the embedded representations, based on the labeled samples in the proximity. The collaborative learning of graph-formulation and classification facilitated the consideration of non-i.i.d. nature of the VI curve samples. The proposed and benchmark methods were analyzed using the VI curves collected over three farms, covering multiple crops, including wheat, barley, and potato crops. The use of similarity computation based on dynamic time warping and interpolated convolution, in addition to the consideration of sample correlation, resulted in significant accuracy improvement as compared to the baseline approaches.