Cross Domain Early Crop Mapping using CropGAN and CNN Classifier
Yiqun Wang Hui Huang Radu State
Driven by abundant satellite imagery, machine learning-based approaches have
recently been promoted to generate high-resolution crop cultivation maps to
support many agricultural applications. One of the major challenges faced by
these approaches is the limited availability of ground truth labels. In the
absence of ground truth, existing work usually adopts the "direct transfer
strategy" that trains a classifier using historical labels collected from other
regions and then applies the trained model to the target region. Unfortunately,
the spectral features of crops exhibit inter-region and inter-annual
variability due to changes in soil composition, climate conditions, and crop
progress, the resultant models perform poorly on new and unseen regions or
years. This paper presents the Crop Generative Adversarial Network (CropGAN) to
address the above cross-domain issue. Our approach does not need labels from
the target domain. Instead, it learns a mapping function to transform the
spectral features of the target domain to the source domain (with labels) while
preserving their local structure. The classifier trained by the source domain
data can be directly applied to the transformed data to produce high-accuracy
early crop maps of the target domain. Comprehensive experiments across various
regions and years demonstrate the benefits and effectiveness of the proposed
approach. Compared with the widely adopted direct transfer strategy, the F1
score after applying the proposed CropGAN is improved by 13.13% - 50.98%