Crop-Net: A Novel Deep Learning Framework for Crop Classification using Time-series Sentinel-1 Imagery by Google Earth Engine

Abstract

Abstract Agricultural land management relies heavily on accurate and timely estimation of uncultivated land. Geographical heterogeneity limits the ability of the model to map crops at large scales. This is because the spectral profile of a crop varies spatially. In addition, the generation of robust deep features from remotely sensed SAR data sets is limited by the conventional deep learning models (lacks a mechanism for informative representation). To address these issues, this study proposes a novel dual-stream framework by combining convolutional neural network (CNN) and nested hierarchical transformer (NesT). Based on a hierarchical transformer structure and convolutional layers with spatial/spectral attention modules, the proposed deep learning framework, called Crop-Net, was designed. Time-series Sentinel-1 SAR data were used to evaluate the performance of the proposed model. Sample datasets were also collected by field survey in ten classes including non-crop classes (i.e. water, built-up and barren) and agricultural crop classes (i.e. arboretum, alfalfa, agricultural-vegetable, broad-bean, barley, canola and wheat). The effectiveness of the Crop-Net model was compared with other advanced machine learning and deep learning frameworks. The proposed Crop-Net model is shown to outperform other models through numerical analysis and visual interpretation of crop classification results. It provides accuracy of more than 98.6 (%) and 0.983 in terms of overall accuracy and kappa coefficient, respectively.