Tensor Canonical Correlation Analysis Networks for Multi view Remote Sensing Scene Recognition
Convolutional neural network (CNN) has been proven an effective way to extract high level features from remote sensing (RS) images automatically. Many variants of the CNN model have been proposed, including principal component analysis network (PCANet) canonical correlation analysis network (CCANet) multiple scale CCANet (MS CCANet) and multiview CCANet (MCCANet) The PCANet is specialized for single view feature abstraction, while in many real world practices, the RS data are frequently observed from many more views. Although CCANet, MS CCANet and MCCANet can be applied to two or more view data, they consider only the pair wise correlation by calculating a series of two order covariance matrices. Moreover, it remains undiscovered how to handle high order consistencies which can only be explored by simultaneously examining all views. In this paper, we propose the tensor canonical correlation analysis network (TCCANet) to tackle this problem. Particularly, TCCANet learns filter banks by simultaneously maximizing arbitrary number of views with high order correlation and solves the optimization problem by decomposing a covariance tensor. After the convolutional stage, we utilize binarization and block wise histogram strategies to generate the final feature. Numerical experiment results on RSSCN7 and SAT 6 datasets demonstrate the advantages of TCCANet and MS TCCANet for RS scene recognition.