Dual and Single Polarized SAR Image Classification Using Compact Convolutional Neural Networks

Abstract

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.retrieval with Sentinel-1 [6], TerraSAR-X, and COSMO-Skymed [7]. A comprehensive list of fields and applications of SAR is available in [8].Ecological and socioeconomic applications greatly benefit from LU/LC classification, making SAR image classification the primary task. For example, forest biomass analysis investigated in [9] provides vegetation ecosystem analysis in Mediterranean areas. Further studies [10,11] focus on the relation between vegetation type and urban climate by questioning how vegetation types affect the temperature. Moreover, Mennis [12] analyzes the relationship between socioeconomic status and vegetation intensity and reveals that higher vegetation intensity is associated with socioeconomic advantage. However, accurate LU/LC classification is a challenging task especially for conventional machine learning methods due to several reasons: (1) existing speckle noise in SAR data, (2) requirement of pre-processing, i.e., feature extraction is especially needed for single-and dual-polarimetric cases to compensate for the lack of full polarization information, and finally, (3) the large-scale nature of SAR data.Nevertheless, there...

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