Cross-regional oil palm tree counting and detection via multi-level attention domain adaptation network

by   Juepeng Zheng, et al.

Providing an accurate evaluation of palm tree plantation in a large region can bring meaningful impacts in both economic and ecological aspects. However, the enormous spatial scale and the variety of geological features across regions has made it a grand challenge with limited solutions based on manual human monitoring efforts. Although deep learning based algorithms have demonstrated potential in forming an automated approach in recent years, the labelling efforts needed for covering different features in different regions largely constrain its effectiveness in large-scale problems. In this paper, we propose a novel domain adaptive oil palm tree detection method, i.e., a Multi-level Attention Domain Adaptation Network (MADAN) to reap cross-regional oil palm tree counting and detection. MADAN consists of 4 procedures: First, we adopted a batch-instance normalization network (BIN) based feature extractor for improving the generalization ability of the model, integrating batch normalization and instance normalization. Second, we embedded a multi-level attention mechanism (MLA) into our architecture for enhancing the transferability, including a feature level attention and an entropy level attention. Then we designed a minimum entropy regularization (MER) to increase the confidence of the classifier predictions through assigning the entropy level attention value to the entropy penalty. Finally, we employed a sliding window-based prediction and an IOU based post-processing approach to attain the final detection results. We conducted comprehensive ablation experiments using three different satellite images of large-scale oil palm plantation area with six transfer tasks. MADAN improves the detection accuracy by 14.98 average F1-score compared with the Baseline method (without DA), and performs 3.55


page 4

page 12

page 21

page 25

page 26

page 27

page 28

page 32


Domain Adaptation on Semantic Segmentation with Separate Affine Transformation in Batch Normalization

In recent years, unsupervised domain adaptation (UDA) for semantic segme...

Revisiting Batch Normalization For Practical Domain Adaptation

Deep neural networks (DNN) have shown unprecedented success in various c...

STNet: Scale Tree Network with Multi-level Auxiliator for Crowd Counting

Crowd counting remains a challenging task because the presence of drasti...

Multi-level Domain Adaptation for Lane Detection

We focus on bridging domain discrepancy in lane detection among differen...

PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation

Domain Adaptation (DA) approaches achieved significant improvements in a...

Multi-Level Adaptive Region of Interest and Graph Learning for Facial Action Unit Recognition

In facial action unit (AU) recognition tasks, regional feature learning ...

Please sign up or login with your details

Forgot password? Click here to reset