The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-923-2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-923-2021
28 Jun 2021
 | 28 Jun 2021

TOWARDS LIFELONG CROP RECOGNITION USING FULLY CONVOLUTIONAL RECURRENT NETWORKS AND SAR IMAGE SEQUENCES

J. A. Chamorro, R. Q. Feitosa, P. N. Happ, and J. D. Bermudez

Keywords: fully convolutional recurrent networks, crop recognition, SAR, remote sensing

Abstract. Recent works have studied crop recognition in regions with highly complex spatio-temporal dynamics typical of a tropical climate. However, most proposals have only been evaluated in a single agricultural year, and their capabilities to generalize to dates outside the temporal sequence have not been properly addressed thus far. This work assesses the generalization capabilities of a recent convolutional recurrent architecture, testing it in a temporal sequence two years ahead of the sequence with which it was trained. Furthermore, a N-to-1 variant of such network is proposed, which is able to produce classification outcomes for every month in the agricultural year, and it is compared with two baselines designed in a more traditional approach, in which a separate specific network is trained for each month of the year. The approaches are evaluated on two public datasets from a tropical region. The first dataset comprehends the period from June 2017 to May 2018, while the second goes from October 2019 to September 2020. Results show a decrease of up to 24.6% in per-date average F1 score when training the network with data of an agricultural year different from the one it is tested on, which indicates a domain shift that demands further research. Additionally, the proposed approach presented only a slight decrease in performance compared to its baseline when trained on the same dataset, with a 2.7% drop in average F1 score. This performance drop is a small cost in exchange for its operational advantages, such as reduced training time and a more straightforward pipeline.