A Convolutional Neural Network for Coastal Aquaculture Extraction from High-Resolution Remote Sensing Imagery

Abstract

Aquaculture has important economic and environmental benefits. With the development of remote sensing and deep learning technology, coastline aquaculture extraction has achieved rapid, automated, and high-precision production. However, some problems still exist in extracting large-scale aquaculture based on high-resolution remote sensing images: (1) the generalization of large-scale models caused by the diversity of remote sensing in breeding areas; (2) the confusion of breeding target identification caused by the complex background interference of land and sea; (3) the boundary of the breeding area is difficult to extract accurately. In this paper, we built a comprehensive sample database based on the spatial distribution of aquaculture, and expanded the sample database by using confusing land objects as negative samples. A multi-scale-fusion superpixel segmentation optimization module is designed to solve the problem of inaccurate boundaries, and a coastal aquaculture network is proposed. Based on the coastline aquaculture dataset that we labelled and produced ourselves, we extracted cage culture areas and raft culture areas near the coastline of mainland China based on high-resolution remote sensing images. The overall accuracy reached 94.64% and achieved a state-of-the-art performance.