Spatio-temporal Crop Classification On Volumetric Data
This work addresses crop classification for agricultural monitoring, but it is incremental as it builds on existing deep learning approaches with modest gains.
The paper tackles large-area crop classification by proposing a novel CNN architecture that combines 3D CNN for spatio-temporal analysis and 1D CNN for temporal analysis, achieving a 2% improvement in classification accuracy over existing methods while maintaining low parameters and inference time.
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.