  Remote Sensing Scene Classification is a challenging and valuable research
topic, in which Convolutional Neural Network (CNN) has played a crucial role.
CNN can extract hierarchical convolutional features from remote sensing
imagery, and Feature Fusion of different layers can enhance CNN's performance.
Two successful Feature Fusion methods, Add and Concat, are employed in certain
state-of-the-art CNN algorithms. In this paper, we propose a novel Feature
Fusion algorithm, which unifies the aforementioned methods using the Kronecker
Product (KPFF), and we discuss the Backpropagation procedure associated with
this algorithm. To validate the efficacy of the proposed method, a series of
experiments are designed and conducted. The results demonstrate its
effectiveness of enhancing CNN's accuracy in Remote sensing scene
classification.
