Designating special areas can reduce pollution caused by ships via adopting special compulsory methods. This paper quantitatively analyzes the types and feasibility of designating special areas in China. Firstly, the paper embeds the category, location, and direction of ocean currents of the functional area in specific sea areas by using Geo-Graphic Information System (GIS) to simulate the types of coastal sea area in China efficiently. Secondly, the fishing net data of 1km*1km in China sea area is established, and the ship point information in specific sea area is extracted by Automatic Identification System (AIS), and the ship density map is formed after the hierarchical rendering of fishing nets. Finally, classification rendering and transparent rendering are carried out on the vectorized spatial data and ship density of simulation respectively, to get the ship aggregation map of the functional area of the researched sea areas. By studying the characteristics of ship aggregation maps in various sea areas and combining with the international experience and methods of designating emission control areas, this paper puts forward a scheme of designating special areas in the Bohai Sea and its offshore waters, southeast coastal waters, and Guangxi Beibu Gulf waters, which has important reference value for China to apply for designating special areas in the future.
Hyperspectral remote sensing provides an outstanding tool in oil slick detection and classification, for its advantages in abundant spectral information. Many classification methods have been proposed and tested for oil spill extraction using hyperspectral images. However, the deep learning method were hardly researched to classify oil slicks using hyperspectral images. In this work, we proposed a spatial-spectral jointed Stacked Auto-encoder (SSAE) to extract and classify oil slicks on the sea surface. The traditional machine learning methods, Support Vector Machine (SVM), Back Propagation Neural network (BPNN) and Stacked Auto-encoder (SAE), were also adopted. The experimental results reveal that our proposed SSAE model can remarkably outperform the other models, especially for the thick oil films. The results of this work could provide an alternative method to extract oil slicks on hyperspectral remote sensing images.
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