  Mapping crops using remote sensing technology is important for food security
and land management. Machine learning-based methods has become a popular
approach for crop mapping in recent years. However, the key to machine
learning, acquiring ample and accurate samples, is usually time-consuming and
laborious. To solve this problem, a crop mapping method in the small sample/no
sample case that integrating domain knowledge and using a cascaded
classification framework that combine a weak classifier learned from samples
with strong features and a strong classifier trained by samples with weak
feature was proposed. First, based on the domain knowledge of various crops, a
low-capacity classifier such as decision tree was applied to acquire those
pixels with distinctive features and complete observation sequences as "strong
feature" samples. Then, to improve the representativeness of these samples,
sample augmentation strategy that artificially remove the observations of
"strong feature" samples according to the average valid observation proportion
in target area was applied. Finally, based on the original samples and
augmented samples, a large-capacity classifier such as random forest was
trained for crop mapping. The method achieved an overall accuracy of 82% in the
MAP crop recognition competition held by Syngenta Group, China in 2021 (third
prize, ranked fourth). This method integrates domain knowledge to overcome the
difficulties of sample acquisition, providing a convenient, fast and accurate
solution for crop mapping.
