LAUSR

Providing accurate and timely oil palm information on a large scale is essential for both economic development and ecological significance. However, owing to different sensors, photograph acquisition conditions, and environmental heterogeneity, the large volume and the variety of the data make it extremely challenging for large-scale and cross-regional oil palm tree detection. It is computationally expensive to train a model from images covering large heterogeneous regions and all environmental conditions for continuously accumulated multisource remote sensing data. In this letter, we propose a new multisource domain generalization (DG) method, Maximum Mean Discrepancy Deep Reconstruction Classification Network (MMD-DRCN). It learns representations from multiple source domains and obtains inspiring performance in an unknown and “unseen” target domain. Besides classification loss, our MMD-DRCN distills more representative features through reconstruction loss and aligns multisource latent features by MMD loss, both of which effectively enhance the capacity of generalization. MMD-DRCN achieves an average F1-score of 82.70% in all transfer tasks, attaining a 5.83% gain compared to Baseline (a straightforward convolutional neural network (CNN) model). Experimental results demonstrate DG poses a promising potential for large-scale and cross-regional oil palm tree detection without any information of the target domain.