Crop mapping in the small sample/no sample case: an approach using a two-level cascade classifier and integrating domain knowledge

TL;DR

This paper presents a crop mapping approach that effectively handles small or no sample scenarios by integrating domain knowledge with a two-level cascade classifier, combining weak and strong classifiers for improved accuracy.

Contribution

The proposed method innovatively combines domain knowledge with a cascaded classification framework to perform crop mapping with minimal or no training samples.

Findings

Achieved 82% overall accuracy in a crop recognition competition

Successfully integrated domain knowledge to reduce sample requirements

Provided a fast and accurate crop mapping solution

Abstract

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.