Abstract
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.
Abstract (translated)
远程 sensing场景分类是一个具有挑战性和实用价值的研究课题，其中卷积神经网络（CNN）发挥了关键作用。CNN可以从遥感图像中提取层次卷积特征，而不同层之间的特征融合可以提高CNN的性能。在某些最先进的CNN算法中，使用了两种成功的特征融合方法：Add和Concat。在本文中，我们提出了一种新颖的特征融合算法，该算法使用Kronecker产品（KPFF）将上述方法统一，并讨论了与该算法相关的反向传播过程。为了验证所提出方法的有效性，进行了一系列实验。结果表明，该方法可以显著提高CNN在远程感测场景分类中的准确率。
URL
https://arxiv.org/abs/2402.00036
