Combining Segmentation Network and Nonsubsampled Contourlet Transform for Automatic Marine Raft Aquaculture Area Extraction from Sentinel-1 Images
Combining Segmentation Network and Nonsubsampled Contourlet Transform for Automatic Marine Raft Aquaculture Area Extraction from Sentinel-1 Images
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DOI：
10.3390/rs12244182
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发表时间：
2020-12
期刊：
影响因子：
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通讯作者：
Yi Zhang;Chengyi Wang;Y. Ji;Jingbo Chen;Yupeng Deng;J. Chen;Yongshi Jie
中科院分区：
文献类型：
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作者：
Yi Zhang;Chengyi Wang;Y. Ji;Jingbo Chen;Yupeng Deng;J. Chen;Yongshi Jie
海筏水产养殖在海洋经济和生态系统中发挥着重要作用。MFA监测具有覆盖面积大、海域分布稀疏的特点，存在野外调查效率低、光学卫星影像数据差等问题。合成孔径雷达(SAR)卫星图像目前被认为是一种有效的数据源，而最先进的方法需要在专业经验的指导下进行手动参数调整。为了克服这一局限性，本文提出了一种结合非下采样轮廓线变换(NSCT)的分割网络来提取Sentinel-1图像的MFA区域。该方法在MFA特征分析的基础上进行了几点改进。首先，应用NSCT增强轮廓和方向特征。其次，引入了多尺度和非对称卷积，以更有效地适应多尺寸和条状特征。第三，在网络结构中同时采用通道和空间注意模块，以克服边界模糊和区域不完整的问题。实验表明，该方法能够有效地提取出海筏养殖区。虽然需要进一步研究以克服过大海浪的干扰问题，但本文提供了一种高效率和可接受的精度的大面积定期监测MFA的方法。
Marine raft aquaculture (MFA) plays an important role in the marine economy and ecosystem. With the characteristics of covering a large area and being sparsely distributed in sea area, MFA monitoring suffers from the low efficiency of field survey and poor data of optical satellite imagery. Synthetic aperture radar (SAR) satellite imagery is currently considered to be an effective data source, while the state-of-the-art methods require manual parameter tuning under the guidance of professional experience. To preclude the limitation, this paper proposes a segmentation network combined with nonsubsampled contourlet transform (NSCT) to extract MFA areas using Sentinel-1 images. The proposed method is highlighted by several improvements based on the feature analysis of MFA. First, the NSCT was applied to enhance the contour and orientation features. Second, multiscale and asymmetric convolutions were introduced to fit the multisize and strip-like features more effectively. Third, both channel and spatial attention modules were adopted in the network architecture to overcome the problems of boundary fuzziness and area incompleteness. Experiments showed that the method can effectively extract marine raft culture areas. Although further research is needed to overcome the problem of interference caused by excessive waves, this paper provides a promising approach for periodical monitoring MFA in a large area with high efficiency and acceptable accuracy.