Marine aquaculture mapping using GF-1 WFV satellite images and full resolution cascade convolutional neural network

Abstract

Growing demand for seafood and reduced fishery harvests have raised intensive farming of marine aquaculture in coastal regions, which may cause severe coastal water problems without adequate environmental management. Effective mapping of mariculture areas is essential for the protection of coastal environments. However, due to the limited spatial coverage and complex structures, it is still challenging for traditional methods to accurately extract mariculture areas from medium spatial resolution (MSR) images. To solve this problem, we propose to use the full resolution cascade convolutional neural network (FRCNet), which maintains effective features over the whole training process, to identify mariculture areas from MSR images. Specifically, the FRCNet uses a sequential full resolution neural network as the first-level subnetwork, and gradually aggregates higher-level subnetworks in a cascade way. Meanwhile, we perform a repeated fusion strategy so that features can receive information from different subnetworks simultaneously, leading to rich and representative features. As a result, FRCNet can effectively recognize different kinds of mariculture areas from MSR images. Results show that FRCNet obtained better performance than other classical and recently proposed methods. Our developed methods can provide valuable datasets for large-scale and intelligent modeling of the marine aquaculture management and coastal zone planning.

Keywords

• mariculture areas
• gaofen-1 wide-field-of-view images
• fully convolutional neural networks
• deep learning