基于深度学习的机载激光海洋测深海陆波形分类

机载激光雷达的海陆波形分类对于沿海地区及其变化性质的研究至关重要。提出了一种在原始的机载激光雷达回波上使用深度学习进行分类的方法。构建全连接神经网络和一维卷积神经网络（CNN），在一个测量海域的数据集上进行训练和测试，最优模型获得了99.6%的分类精度。该最优模型对来自不同测量海域的数据进行分类，分类精度达到了95.6%，相比支持向量机方法，处理速度提高了约52%。结果表明:深度学习方法对机载激光雷达回波波形的分类具有较高的精度和速度，它可以进一步作为通过机载激光测深技术对海底种类进行分类的候选方法。

Classification of sea and land waveforms based on deep learning for airborne laser bathymetry

Classification of sea and land returns in airborne lidar was essential for the research of coastal zones and their changing nature. A method for classification using deep learning on the original airborne lidar echo was proposed. A fully connected neural network, and a one-dimensional convolutional neural network (CNN), were used on a training dataset and test datasets from in-situ measurements, and a classification accuracy of 99.6% was obtained. The model was utilized on the datasets from different areas, a classification accuracy of 95.6% was achieved and the processing speed was increased by about 52% compared to support vector machine (SVM) method. The results denote that the deep learning method is very effective for classification of airborne lidar echo waveforms with high precision and speed. It may present further use as a candidate method for classifying species on the sea floor with airborne laser bathymetry.