Transfer Learning for Crop Classification in Data-Scarce Regions Using Satellite Imagery

Satellite imagery provides valuable data to address the growing demand for agricultural production. However, analyzing such vast amounts of data requires advanced artificial intelligence methods, such as deep learning. The primary challenge lies in the scarcity of labeled training data, as its preparation is both costly and time-consuming. To address this issue, this study integrates remote sensing data, deep neural networks, and transfer learning techniques to estimate the cultivated area of strategic crops in Iran. Given the diverse climates and topographies across Iran’s provinces, in addition to Sentinel-1 and Sentinel-2 satellite data, MODIS sensor and SRTM elevation data were also utilized. To compensate for data limitations, transfer learning was employed to enhance model performance in data-deficient regions (Kermanshah and Markazi). This approach resulted in an approximate 10% improvement in Cohen’s Kappa coefficient. Furthermore, the study investigated the minimum data required for fine-tuning the models. The results demonstrated that even with a reduction of over 60% in the target province's training data, transfer learning still achieved model performance comparable to scenarios where it was not applied.