Very high resolution Airborne PolSAR Image Classification using Convolutional Neural Networks
This work addresses classification challenges in remote sensing for applications like environmental monitoring, but it is incremental as it adapts existing CNN methods to a specific dataset.
The paper tackled the classification of very high resolution polarimetric SAR images by using convolutional neural networks, specifically SegNet, and achieved significant performance improvements by incorporating structural tensors alongside polarimetric features.
In this work, we exploit convolutional neural networks (CNNs) for the classification of very high resolution (VHR) polarimetric SAR (PolSAR) data. Due to the significant appearance of heterogeneous textures within these data, not only polarimetric features but also structural tensors are exploited to feed CNN models. For deep networks, we use the SegNet model for semantic segmentation, which corresponds to pixelwise classification in remote sensing. Our experiments on the airborne F-SAR data show that for VHR PolSAR images, SegNet could provide high accuracy for the classification task; and introducing structural tensors together with polarimetric features as inputs could help the network to focus more on geometrical information to significantly improve the classification performance.