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首页> 外文OA文献 >Coastal Wetland Mapping with Sentinel-2 MSI Imagery Based on Gravitational Optimized Multilayer Perceptron and Morphological Attribute Profiles
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Coastal Wetland Mapping with Sentinel-2 MSI Imagery Based on Gravitational Optimized Multilayer Perceptron and Morphological Attribute Profiles

机译：基于引力优化的多层的Multidayer Perceptron和形态属性配置文件的沿海湿地映射与哨兵-2 MSI图像

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Coastal wetland mapping plays an essential role in monitoring climate change, the hydrological cycle, and water resources. In this study, a novel classification framework based on the gravitational optimized multilayer perceptron classifier and extended multi-attribute profiles (EMAPs) is presented for coastal wetland mapping using Sentinel-2 multispectral instrument (MSI) imagery. In the proposed method, the morphological attribute profiles (APs) are firstly extracted using four attribute filters based on the characteristics of wetlands in each band from Sentinel-2 imagery. These APs form a set of EMAPs which comprehensively represent the irregular wetland objects in multiscale and multilevel. The EMAPs and original spectral features are then classified with a new multilayer perceptron (MLP) classifier whose parameters are optimized by a stability-constrained adaptive alpha for a gravitational search algorithm. The performance of the proposed method was investigated using Sentinel-2 MSI images of two coastal wetlands, i.e., the Jiaozhou Bay and the Yellow River Delta in Shandong province of eastern China. Comparisons with four other classifiers through visual inspection and quantitative evaluation verified the superiority of the proposed method. Furthermore, the effectiveness of different APs in EMAPs were also validated. By combining the developed EMAPs features and novel MLP classifier, complicated wetland types with high within-class variability and low between-class disparity were effectively discriminated. The superior performance of the proposed framework makes it available and preferable for the mapping of complicated coastal wetlands using Sentinel-2 data and other similar optical imagery.
机译：沿海湿地绘图在监测气候变化，水文周期和水资源中起着重要作用。在本研究中，基于引力优化多层Perceptron分类器和扩展多属性配置文件（映射）的新型分类框架用于沿海湿地映射，使用Sentinel-2多光谱仪器（MSI）图像。在所提出的方法中，基于来自Sentinel-2图像的每个频带中的湿地的特征，首先利用四个属性滤波器提取形态属性分布（AP）。这些APS形成一组映射，全面地代表多尺度和多级的不规则湿地物体。然后，使用新的多层的Perceptron（MLP）分类器对其进行分类，其参数由用于引力搜索算法的稳定受限的自适应α进行优化。使用两个沿海湿地，即胶州湾和中国东部山东省黄河三角洲的Sentinel-2 MSI图像进行了研究的表现。通过视觉检查和定量评估与四种其他分类器的比较验证了所提出的方法的优越性。此外，还验证了不同APS在映射中的有效性。通过组合发育的映射特征和新颖的MLP分类器，有效地区分了具有高级别变异性和级别差距低的复杂的湿地类型。所提出的框架的卓越性能使得可用，并且优选使用Sentinel-2数据和其他类似的光学图像来映射复杂的沿海湿地。

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