From image-level to pixel-level labeling: A weakly-supervised learning method for identifying aquaculture ponds using iterative anti-adversarial attacks guided by aquaculture features

Abstract

Aquaculture mapping is essential for monitoring and managing aquaculture resources. However, accurately geotargeting individual aquaculture ponds from medium-resolution remote sensing imagery remains challenging, and convolutional deep learning methods for identifying aquaculture ponds require labor-intensive pixel-level annotations. This paper presents a novel weakly-supervised learning method to derive pixel-level labels from image-level annotations for aquaculture ponds. Our approach uses iterative anti-adversarial attacks to refine localization results from multi-scale class activation maps (CAMs). The improved method integrates two regularization methods guided by aquaculture features to form a joint loss function for adversarial samples: discriminative water region suppression and non-aquaculture water class suppression. We also propose an aquaculture feature termed CFNDWI to constrain the localization results and generate high-quality pseudo-labels. As a result, the pseudo-labels are used to train semantic segmentation networks for accurately identifying aquaculture ponds. We evaluated the performance of our method using commonly-used backbones on 10 m Sentinel-2 imagery. Our method achieves Intersection over Union (IoU) values of 0.618–0.655 for pseudo-label generation, and IoU values of 0.664–0.708 for semantic segmentation, outperforming state-of-the-art weakly-supervised methods and public datasets. The effectiveness of each module of our method was also testified through ablation experiments. Our method leverages knowledge-driven aquaculture features to guide the data-driven adversarial learning process, addressing the lack of high-quality aquaculture datasets for model training. The code for implementing our method will be accessible at https://github.com/designer1024/WSLM-AQ.

Keywords

• Aquaculture pond
• Weakly-supervised learning
• Adversarial learning
• Pseudo-label
• Class activation map