Dual and Single Polarized SAR Image Classification Using Compact Convolutional Neural Networks
Dual and Single Polarized SAR Image Classification Using Compact Convolutional Neural Networks
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DOI：
10.3390/rs11111340
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发表时间：
2019-06
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通讯作者：
Mete Ahishali;S. Kiranyaz;T. Ince;M. Gabbouj
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作者：
Mete Ahishali;S. Kiranyaz;T. Ince;M. Gabbouj
合成孔径雷达（SAR）图像的准确土地利用/土地覆盖分类在环境、经济和自然相关研究领域和应用中发挥着重要作用。在无法获得全极化合成孔径雷达数据的情况下，也可使用单极化或双极化合成孔径雷达数据，但会造成某些困难。例如，传统的机器学习（ML）方法通常专注于寻找更具鉴别力的特征，以克服由于单偏振或双偏振而导致的信息缺乏。除了传统的ML方法之外，提出深度卷积神经网络（CNN）的研究也存在局限性和缺点，例如需要大量数据进行训练，以及需要特殊硬件来实现复杂的深度网络。在这项研究中，我们提出了一种基于滑动窗口分类的系统方法，该方法具有紧凑和自适应的CNN，可以克服这些缺点，同时实现土地利用/土地覆盖分类的最新性能水平。所提出的方法完全不需要特征提取和选择过程，并直接对SAR强度数据进行分类。此外，与深度CNN不同的是，所提出的方法既不需要专用硬件，也不需要大量带有地面真实标签的数据。所提出的系统方法的目的是实现最大的分类精度的单和双偏振强度数据与最小的人类互动。此外，由于其紧凑的配置，所提出的方法可以处理这样的小补丁，这是深度学习解决方案无法实现的。这种能力显著改善了分割掩码中的细节。在两个基准SAR数据集上的大量实验证实了所提出的方法与竞争方法相比具有上级分类性能和有效的计算复杂度。
Accurate land use/land cover classification of synthetic aperture radar (SAR) images plays an important role in environmental, economic, and nature related research areas and applications. When fully polarimetric SAR data is not available, single- or dual-polarization SAR data can also be used whilst posing certain difficulties. For instance, traditional Machine Learning (ML) methods generally focus on finding more discriminative features to overcome the lack of information due to single- or dual-polarimetry. Beside conventional ML approaches, studies proposing deep convolutional neural networks (CNNs) come with limitations and drawbacks such as requirements of massive amounts of data for training and special hardware for implementing complex deep networks. In this study, we propose a systematic approach based on sliding-window classification with compact and adaptive CNNs that can overcome such drawbacks whilst achieving state-of-the-art performance levels for land use/land cover classification. The proposed approach voids the need for feature extraction and selection processes entirely, and perform classification directly over SAR intensity data. Furthermore, unlike deep CNNs, the proposed approach requires neither a dedicated hardware nor a large amount of data with ground-truth labels. The proposed systematic approach is designed to achieve maximum classification accuracy on single and dual-polarized intensity data with minimum human interaction. Moreover, due to its compact configuration, the proposed approach can process such small patches which is not possible with deep learning solutions. This ability significantly improves the details in segmentation masks. An extensive set of experiments over two benchmark SAR datasets confirms the superior classification performance and efficient computational complexity of the proposed approach compared to the competing methods.