ICIP2021 – Spatio-Temporal Crop Classification On Volumetric Data

M. U. Qadeer, S. Saeed, M. Taj and A. Muhammad

Abstract:

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.

Resources
PDF: Paper

Text Reference:

M. U. Qadeer, S. Saeed, M. Taj and A. Muhammad, 
"Spatio-Temporal Crop Classification On Volumetric Data," 
IEEE International Conference on Image Processing (ICIP), 2021, pp. 3812-3816, 
doi: 10.1109/ICIP42928.2021.9506046.

Bibtex Reference:

@INPROCEEDINGS{TajICIP2021_2,
  author={Qadeer, Muhammad Usman and Saeed, Salar and Taj, Murtaza and Muhammad, Abubakr},
  booktitle={2021 IEEE International Conference on Image Processing (ICIP)}, 
  title={Spatio-Temporal Crop Classification On Volumetric Data}, 
  year={2021},
  volume={},
  number={},
  pages={3812-3816},
  doi={10.1109/ICIP42928.2021.9506046}}