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首页> 外文期刊>Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of >Deep Convolutional Neural Network for Complex Wetland Classification Using Optical Remote Sensing Imagery
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Deep Convolutional Neural Network for Complex Wetland Classification Using Optical Remote Sensing Imagery

机译：利用光学遥感影像对复杂湿地进行分类的深度卷积神经网络

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摘要

The synergistic use of spatial features with spectral properties of satellite images enhances thematic land cover information, which is of great significance for complex land cover mapping. Incorporating spatial features within the classification scheme have been mainly carried out by applying just low-level features, which have shown improvement in the classification result. By contrast, the application of high-level spatial features for classification of satellite imagery has been underrepresented. This study aims to address the lack of high-level features by proposing a classification framework based on convolutional neural network (CNN) to learn deep spatial features for wetland mapping using optical remote sensing data. Designing a fully trained new convolutional network is infeasible due to the limited amount of training data in most remote sensing studies. Thus, we applied fine tuning of a pre-existing CNN. Specifically, AlexNet was used for this purpose. The classification results obtained by the deep CNN were compared with those based on well-known ensemble classifiers, namely random forest (RF), to evaluate the efficiency of CNN. Experimental results demonstrated that CNN was superior to RF for complex wetland mapping even by incorporating the small number of input features (i.e., three features) for CNN compared to RF (i.e., eight features). The proposed classification scheme is the first attempt, investigating the potential of fine-tuning pre-existing CNN, for land cover mapping. It also serves as a baseline framework to facilitate further scientific research using the latest state-of-art machine learning tools for processing remote sensing data.
机译：空间特征与卫星图像光谱特性的协同使用可增强主题土地覆盖信息，这对于复杂的土地覆盖制图具有重要意义。将空间特征合并到分类方案中主要是通过仅应用低级特征来进行的，这已显示出分类结果的改进。相比之下，用于卫星图像分类的高级空间特征的应用却不足。这项研究旨在通过提出基于卷积神经网络（CNN）的分类框架，以利用光学遥感数据学习湿地地图的深层空间特征，从而解决高级特征的缺失。由于大多数遥感研究中训练数据量有限，因此设计一个训练有素的新卷积网络是不可行的。因此，我们对现有的CNN进行了微调。具体来说，AlexNet用于此目的。将深层CNN的分类结果与基于众所周知的整体分类器即随机森林（RF）的分类结果进行比较，以评估CNN的效率。实验结果表明，即使在CNN的输入特征数量较少（即三个特征）的情况下，相比于RF（八个特征），对于复杂的湿地测绘，CNN仍优于RF。拟议的分类方案是首次尝试，以研究对现有CNN进行微调的潜力，以进行土地覆被制图。它还用作使用最新的机器学习工具来处理遥感数据的基础框架，以促进进一步的科学研究。

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