Multi-Year Vector Dynamic Time Warping Based Crop Mapping
Mustafa Teke, Yasemin Yard{\i}mc{\i}

TL;DR
This paper introduces Vector Dynamic Time Warping (VDTW), a novel multi-year crop classification method that effectively handles temporal and spectral variations, achieving near-perfect accuracy in cross-year crop mapping using Landsat time-series data.
Contribution
The paper proposes VDTW, a new warping-based classification approach for multi-year crop mapping that outperforms existing methods in accuracy and robustness across different years and regions.
Findings
VDTW achieved over 99.7% accuracy in cross-year crop mapping.
The method is robust to temporal, spectral, and measurement variations.
Fewer training samples are needed compared to other approaches.
Abstract
Recent automated crop mapping via supervised learning-based methods have demonstrated unprecedented improvement over classical techniques. However, most crop mapping studies are limited to same-year crop mapping in which the present year's labeled data is used to predict the same year's crop map. Classification accuracies of these methods degrade considerably in cross-year mapping. Cross-year crop mapping is more useful as it allows the prediction of the following years' crop maps using previously labeled data. We propose Vector Dynamic Time Warping (VDTW), a novel multi-year classification approach based on warping of angular distances between phenological vectors. The results prove that the proposed VDTW method is robust to temporal and spectral variations compensating for different farming practices, climate and atmospheric effects, and measurement errors between years. We also…
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Taxonomy
MethodsDynamic Time Warping
