Oil palm plantation mapping from high-resolution remote sensing images using deep learning

Abstract

Oil palm plantation mapping is an important task in land planning and management in Malaysia. Most existing studies were based on satellite images using traditional machine learning or image segmentation methods. In order to obtain finer oil palm plantation maps from high spatial-resolution satellite images, we proposed a novel deep learning-based semantic segmentation approach, named Residual Channel Attention Network (RCANet). It consists of an encoder-decoder architecture and a post-processing component. The Residual Channel Attention Unit (RCAU) designed in our proposed approach reuses the low-level features extracted from the encoder part through upsampling, effectively enhancing the discriminative features and suppressing the indiscriminate features. We extended the fully connected Conditional Random Field (FC-CRF) in the post-processing to further refine the segmentation results. Experiment results were evaluated by our proposed Malaysian Oil Palm Plantation Dataset (MOPPD), which was collected from the Google Earth high spatial-resolution image and published in this article. Our proposed method achieves the overall accuracy (OA) of 96.88% and mean Intersection-over-Union (mean IoU) of 90.58%, improving the OA by 2.03%-3.96% and the mean IoU by 2.13%-5.44% compared with other semantic segmentation methods (i.e. Fully Connected Network, U-Net and Fully Connected DenseNet). In addition, we exhibited the results of the oil palm plantation mapping in large-scale areas (around 320 km2) and demonstrated the effectiveness of our method for large-scale mapping.