Application of Deep Learning Crop Classification Model based on multispectral and SAR satellite imagery

Classifying crops using satellite data is a challenge, especially since most crops have similar growth cycles. Due to their different shapes and chlorophyll content, different crops have subtle differences in their emission spectra in different bands. This study uses a data-driven approach to build a series of deep learning models to classify 36 different land covers in Steele County and Traill Country, North Dakota, US. This study used Google Earth Engine to generate a composite layer containing Sentinel 1 and Sentinel 2 satellite data and surface crop data over the study area. And randomly generated 200,000 sample points on this layer; each sample point contains the value of 12 months of SAR and spectral data. In this way, a two-dimensional feature matrix of the time dimension and spectral band dimension is generated for each sample point. The training dataset of the model is composed of the feature matrix of these sample points, and the surface crops as labels correspond to the feature matrix. Since this is a dataset with two-dimensional features, this research uses four deep learning models: Dense Neural Network (DNN), Long short-term memory (LSTM), Convolutional neural network (CNN) and Transformer. Among them, the Transformer model based on the self-attention mechanism performed the best, with a comprehensive accuracy rate of 85%, and the classification accuracy rate of crops with more than 2,000 sample points in the training data set reached more than 90%.