A low-rank fully convolutional network for classification based on a multi-dimensional description primitive of time series polarimetric sar images

摘要

Time series polarimetric SAR image classification relies on learned understanding of how the set of pixels in an image relate by relative position and how the information of different dates in a time series change as time goes on. In this paper, we firstly integrate the incoherent information in the spatial scale and the coherent information in the temporal scale to form the feature for time series polarimetric SAR images. Then we take advantage of the fully convolutional network (FCN) to make end-to-end, pixels-to-pixels 3-dimensional training, on which base we sparse the connection structure of deep learning network using low rank tensor decomposition to reduce the computational complexity of convolutional layers. Experiment results on a real polarimetric SAR data set preliminarily show the effectiveness of our presented approach.