Spatial and polarimetric information fusion using residual network for polarimetric synthetic aperture radar image classification

Abstract

A polarimetric synthetic aperture radar (POLSAR) system provides an image that can be considered as a data cube containing spatial information in two spatial dimensions and polarimetric information in the scattering dimension. A spatial and polarimetric residual network (SPRN) is proposed for POLSAR image classification. At first, polarimetric features are extracted from the scattering dimension through two designed polarimetric residual blocks. Then, the processed POLSAR cube is fed to two consecutive spatial residual blocks for contextual feature extraction. Three dimensional convolutional layers are used as basic layers for simultaneous extraction and fusion of polarimetric information and correlation among neighbouring pixels in local regions. The shortcut connections are utilised to overcome the degradation problem due to increasing network depth. In addition, batch normalisation is applied to regularise the learning process. The experimental results on four real POLSAR images show the superior performance of SPRN compared to several state‐of‐the‐art classifiers in terms of various assessment measures.

Application of Image Style Transfer Based on Normalized Residual Network in Art Design

“…Therefore, image style transfer (IST) has become a research highlight. IST technology refers to the process of converting an image into an image that is similar in style or content to the target image through machine learning methods and other techniques [2,3]. With the progress of deep learning (DL) technology, some scholars build image style migration methods based on the advantages of Convolutional neural network (CNN) feature extraction, while others propose to build image style migration methods using encoder and decoder models [4].…”