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المؤلفAhishali, Mete
المؤلفKiranyaz, Serkan
المؤلفInce, Turker
المؤلفGabbouj, Moncef
تاريخ الإتاحة2020-06-02T08:17:31Z
تاريخ النشر2019
اسم المنشورRemote Sensing
المصدرScopus
الرقم المعياري الدولي للكتاب20724292
معرّف المصادر الموحدhttp://dx.doi.org/10.3390/rs11111340
معرّف المصادر الموحدhttp://hdl.handle.net/10576/15010
الملخصAccurate 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. - 2019 by the authors.
اللغةen
الناشرMDPI AG
الموضوعConvolutional NeuRal Networks
Land use/land cover classification
Sliding window
Synthetic aperture radar (SAR)
العنوانDual and single polarized sar image classification using compact convolutional neural networks
النوعArticle
رقم العدد11
رقم المجلد11
dc.accessType Open Access


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