Detecting oil palm plantation from high-resolution satellite images can provide the necessary information for palm oil production estimation and oil palm plantation layout planning, etc. In this paper, we proposed a novel semantic segmentation based approach for large-scale oil palm plantation detection using QuickBird images and Google Earth Images (in 0.6-m spatial resolution) in Malaysia. We manually labeled a dataset for pixel-wise semantic segmentation into four categories: oil palm plantation, other vegetation, impervious/cloud, and the others (e.g. water and uncertain pixels). We presented an end-to-end deep convolutional neural network (DCNN) for semantic segmentation followed by fully connected conditional random fields (CRF) and applied an ensemble learning method to improve the localization of boundaries. The overall accuracy and mean IoU of our proposed approach in test regions are 95.27% and 88.46%, which are greatly better than the results of the other three common semantic segmentation methods and patch-based CNN method.
Oil palm tree detection is of great significance for improving the irrigation, estimating the yield of palm oil, and predicting the expansion trend, etc. Existing tree detection methods include traditional image processing, machine learning methods, and sliding window based deep learning methods. In this paper, we proposed a deep learning based end-to-end method for oil palm detection in large scale. First, we built an oil palm sample dataset from 0.1m-resolution Unmanned Aerial Vehicle (UAV) images. Second, we implemented five state-of-the-art object detection algorithms (i.e. Faster- RCNN, VGG-SSD, YOLO-v3, RetinaNet and Mobilenet-SSD) and evaluated their performances for detecting the tree crown size and the location of oil palms. Moreover, we designed an overlapping partition method to improve the oil palm detection results of the UAV images in over 40,000 × 40,000 pixels. Experiment results demonstrate that in terms of the detection accuracy, VGG-SSD achieves the best accuracy of 90.91% on the validation dataset, followed by YOLO-v3, RetinaNet, Mobilenet-SSD and Faster RCNN. Meanwhile, we compared the detection time of the five object detection algorithms. Mobilenet-SSD achieves the highest detection speed among five algorithms (12.81ms per image in 500×500 pixels), with the speedup ratios of 17.5×, 10.2×, 4.51×, and 17.33× compared with Faster-RCNN, VGG-SSD, YOLO-v3 and RetinaNet. The results show that our proposed oil palm detection method is of great practical value to the precision agriculture of the oil palm industry.