  Crop classification using remote sensing data has emerged as a prominent
research area in recent decades. Studies have demonstrated that fusing SAR and
optical images can significantly enhance the accuracy of classification.
However, a major challenge in this field is the limited availability of
training data, which adversely affects the performance of classifiers. In
agricultural regions, the dominant crops typically consist of one or two
specific types, while other crops are scarce. Consequently, when collecting
training samples to create a map of agricultural products, there is an
abundance of samples from the dominant crops, forming the majority classes.
Conversely, samples from other crops are scarce, representing the minority
classes. Addressing this issue requires overcoming several challenges and
weaknesses associated with traditional data generation methods. These methods
have been employed to tackle the imbalanced nature of the training data.
Nevertheless, they still face limitations in effectively handling the minority
classes. Overall, the issue of inadequate training data, particularly for
minority classes, remains a hurdle that traditional methods struggle to
overcome. In this research, We explore the effectiveness of conditional tabular
generative adversarial network (CTGAN) as a synthetic data generation method
based on a deep learning network, in addressing the challenge of limited
training data for minority classes in crop classification using the fusion of
SAR-optical data. Our findings demonstrate that the proposed method generates
synthetic data with higher quality that can significantly increase the number
of samples for minority classes leading to better performance of crop
classifiers.
