Abstract
Coastal aquaculture areas are some of the main areas to obtain marine fishery resources and are vulnerable to storm-tide disasters. Obtaining the information of coastal aquaculture areas quickly and accurately is important for the scientific management and planning of aquaculture resources. Recently, deep neural networks have been widely used in remote sensing to deal with many problems, such as scene classification and object detection, and there are many data sources with different spatial resolutions and different uses with the development of remote sensing technology. Thus, using deep learning networks to extract coastal aquaculture areas often encounters the following problems: (1) the difficulty in labeling; (2) the poor robustness of the model; (3) the spatial resolution of the image to be processed is inconsistent with that of the existing samples. In order to fix these problems, this paper proposes a novel semi-/weakly-supervised method, the semi-/weakly-supervised semantic segmentation network (Semi-SSN), and adopts 3 data sources: GaoFen-2 image, GaoFen-1(PMS)image, and GanFen-1(WFV)image with a 0.8 m, 2 m, and 16 m spatial resolution, respectively, and through experiments, we analyze the extraction effect of the model comprehensively. After comparing with other the-state-of-art methods and verifying on an open remote sensing dataset, we take the Fujian coastal area (mainly Sanduo) as the experimental area and employ our method to detect the effect of storm-tide disasters on coastal aquaculture areas, monitor the production, and make the distribution map of coastal aquaculture areas.
Highlights
IntroductionRecent successful advances of deep learning make it become an increasingly popular choice in many fields of application
Based on the methodology of conditional generative adversarial nets (CGANs) and previous research, this paper proposes a novel network, Semi-SSN, that introduces conditional adversarial learning into the semantic segmentation network to realize semi-/weakly-supervised learning
We used Semi-SSN to extract coastal aquaculture areas in GF-2 images based on semi-supervised method with different labeled
Summary
Recent successful advances of deep learning make it become an increasingly popular choice in many fields of application. Following this wave of success and due to the increased availability of data and computational resources, the usage of deep learning in remote sensing is taking off in remote sensing as well. In order to obtain the information of aquaculture areas, there are more and more researchers paying attention to using remote sensing technology and machine learning, and a series of research works has ensued [1,2,3,4,5,6,7]. Researchers use expert experience [8,9,10], characteristic
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