Enteromorpha Prolifera Detection in High-Resolution Remote Sensing Imagery Based on Boundary-Assisted Dual-Path Convolutional Neural Networks

Enteromorpha prolifera is as a frequent marine ecological environment disaster. How to quickly and accurately monitor Enteromorpha prolifera is of great significance to its management and protection of the marine ecological environment. The detection of Enteromorpha prolifera from high spatial resolution remote sensing images (HSRIs) is an important technical means for monitoring Enteromorpha prolifera disasters. With respect to the difficulty of accurate detection of Enteromorpha prolifera area boundary in HSRIs, this paper proposes an Enteromorpha prolifera detection method for HSRIs based on boundary-assisted dual-path convolutional neural networks (CNN). First, a large-scale HSRIs Enteromorpha prolifera detection dataset, FIO-EP, is created and made publication to facilitate the field of HSRIs Enteromorpha prolifera detection. Then, a boundary-assisted dual-path CNN framework is designed to detect Enteromorpha prolifera in HSRIs based on the shape distribution characteristics of Enteromorpha prolifera. In the CNN framework, accurate detection of Enteromorpha prolifera areas in HSRIs is achieved by fusing initial detection and boundary detection results of Enteromorpha prolifera. The proposed method is compared with some state-of-the-art Enteromorpha prolifera detection algorithms using the FIO-EP dataset. The experimental findings demonstrate that the proposed method can obtain 88.28% F1-score and 79.02% intersection-over-union (IOU), and is superior to other state-of-the-art Enteromorpha prolifera detection algorithms.