Hoang Nhat-Duc
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam.
Faculty of Civil Engineering, Duy Tan University, Da Nang 550000, Vietnam.
Comput Intell Neurosci. 2021 Feb 16;2021:8820116. doi: 10.1155/2021/8820116. eCollection 2021.
Up-to-date information regarding impervious surface is valuable for urban planning and management. The objective of this study is to develop neural computing models used for automatic impervious surface area detection at a regional scale. To achieve this task, advanced optimizers of adaptive moment estimation (Adam), a variation of Adam called Adamax, Nesterov-accelerated adaptive moment estimation (Nadam), Adam with decoupled weight decay (AdamW), and a new exponential moving average variant (AMSGrad) are used to train the artificial neural network models employed for impervious surface detection. These advanced optimizers are benchmarked with the conventional gradient descent with momentum (GDM). Remotely sensed images collected from Sentinel-2 satellite for the study area of Da Nang city (Vietnam) are used to construct and verify the proposed approach. Moreover, texture descriptors including statistical measurements of color channels and binary gradient contour are employed to extract useful features for the neural computing model-based pattern recognition. Experimental result supported by statistical test points out that the Nadam optimizer-based neural computing model has achieved the most desired predictive accuracy for the data collected in the studied region with classification accuracy rate of 97.331%, precision = 0.961, recall = 0.984, negative predictive value = 0.985, and F1 score = 0.972. Therefore, the model developed in this study can be a helpful tool for decision-makers in the task of urban land-use planning and management.
有关不透水表面的最新信息对城市规划和管理很有价值。本研究的目的是开发用于区域尺度自动检测不透水表面积的神经计算模型。为完成此任务，使用了自适应矩估计（Adam）的高级优化器、Adam的一种变体Adamax、Nesterov加速自适应矩估计（Nadam）、带解耦权重衰减的Adam（AdamW）以及一种新的指数移动平均变体（AMSGrad）来训练用于不透水表面检测的人工神经网络模型。这些高级优化器与传统的动量梯度下降（GDM）进行了基准测试。使用从哨兵2号卫星收集的越南岘港市研究区域的遥感图像来构建和验证所提出的方法。此外，包括颜色通道统计测量和二元梯度轮廓的纹理描述符被用于为基于神经计算模型的模式识别提取有用特征。统计测试支持的实验结果表明，基于Nadam优化器的神经计算模型对研究区域收集的数据实现了最理想的预测精度，分类准确率为97.331%，精确率 = 0.961，召回率 = 0.984，阴性预测值 = 0.985，F1分数 = 0.972。因此，本研究中开发的模型可以成为城市土地利用规划和管理任务中决策者的有用工具。
