| Abstract | Classification of SAR images has been an interesting task considering its major role in environmental and natural research areas. Existing studies proposed for Land use/land cover (LU/LC) classification using SAR data can be grouped into two categories: traditional Machine Learning (ML) approaches and approaches that are based on deep Convolutional Neural Networks (CNNs). Traditional ML approaches are generally based on discovering powerful features in terms of discrimination and utilizing different combinations of them. On the other hand, in the latter group, the feature extraction and classification topologies are combined in a single learning framework and they are optimized together. Major drawbacks of the deep CNN methods are that they require a significant amount of annotated data to achieve the desired generalization capability and special hardware, i.e., GPUs for the training and usually inference as well. To address these limitations, employing compact CNNs for the SAR classification is proposed in several existing studies. In this chapter, we investigate the performance of compact CNNs that aim for minimum computational complexity and limited annotated data for the SAR classification. The provided analysis will cover commonly used SAR benchmark datasets consisting of four fully polarimetric, one dual- and one single-polarized SAR data including both spaceborne and airborne sensors. |
