  Large-area crop classification using multi-spectral imagery is a widely
studied problem for several decades and is generally addressed using classical
Random Forest classifier. Recently, deep convolutional neural networks (DCNN)
have been proposed. However, these methods only achieved results comparable
with Random Forest. In this work, we present a novel CNN based architecture for
large-area crop classification. Our methodology combines both spatio-temporal
analysis via 3D CNN as well as temporal analysis via 1D CNN. We evaluated the
efficacy of our approach on Yolo and Imperial county benchmark datasets. Our
combined strategy outperforms both classical as well as recent DCNN based
methods in terms of classification accuracy by 2% while maintaining a minimum
number of parameters and the lowest inference time.
