Classification of dual- and single polarized SAR images by incorporating visual features

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

“…For the cases of multiple polarizations, different target decompositions are used as high-level electromagnetic features, whereas only a single (intensity) channel exists for the single polarization, hence limiting the use of the rich set of electromagnetic features for classification. These studies further reveal that using secondary features such as color and texture [15,18,27,31] can significantly improve the classification performance with an inevitable cost of computational complexity increase.The state-of-the-art classification performance over single-and dual-polarized SAR intensity data has been achieved by a recent study [18] which uses a large ensemble of classifiers over a composite feature vector in high dimensions (e.g., >200-D) with several electromagnetic (primary) and image processing (secondary) features. As a conventional approach, this method also has certain limitations.…”

“…Superpixel based watershed approaches [23] are used with average contrast maximization in [24] for river channel segmentation. On the other hand, recent studies [15,16,18] have shown that supervised methods have significantly better performance compared to unsupervised ones.…”

“…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 have been many existing studies using supervised and unsupervised methods [13][14][15][16][17][18][19][20] for LU/LC classification of SAR images. On the one hand, several clustering methods are proposed [19,20] as the second group and the underlying task is challenging especially for high-resolution SAR images mainly due to the heterogonous regions in the data.…”

“…This requirement may prevent their use in a low-cost and or real-time applications.In this study, in order to address the aforementioned drawbacks and limitations of conventional and Deep Learning methods, we propose a systematic approach for accurate LU/LC classification of single-polarized COSMO-SkyMed and dual-polarized TerraSAR-X intensity data, which are both space-born X-band SAR images, using compact and adaptive CNNs. Performance of the proposed approach will be evaluated against the current state-of-the-art method in SAR image classification [18] and two recently proposed deep CNNs for ImageNet -Large Scale Visual Recognition Challenge [41]: Xception and Inception-Resnet-v2 [36,37]. The novel and significant contributions of the proposed approach can be listed as follows: first, unlike conventional methods, the proposed approach can directly be performed over SAR intensity data without requiring any prior feature extraction or pre-processing steps.…”

“…Nevertheless, there have been many existing studies using supervised and unsupervised methods [13][14][15][16][17][18][19][20] for LU/LC classification of SAR images. On the one hand, several clustering methods are proposed [19,20] as the second group and the underlying task is challenging especially for high-resolution SAR images mainly due to the heterogonous regions in the data.…”