Crop Type Classification based on Machine Learning with Multitemporal Sentinel-1 Data

Jacob Jeppesen

In this paper, we propose a data processing chain for processing multitemporal Sentinel-1 SAR data, and show how the temporal patterns of agricultural fields can be visualized to provide a valuable overview prior to classification. We then investigate the performance of 6 machine learning methods for crop type classification of 12 crop types based on 44333 fields




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