Convolutional neural networks (CNNs) have outstanding advantages in the classification of remote sensing scenes. Deep CNN models with better classification performance typically have high complexity, whereas shallow CNN models with low complexity rarely achieve good classification performance for remote sensing images with complex spatial structures. In this article, we proposed a new lightweight CNN classification method based on branch feature fusion (LCNN-BFF) for remote sensing scene classification. In contrast to a conventional single linear convolution structure, the proposed model had a bilinear feature extraction structure. The BFF method was utilized to fuse the feature information extracted from the two branches, which improved the classification accuracy. In addition, combining depthwise separable convolution and conventional convolution to extract image features greatly reduced the complexity of the model on the premise of ensuring the accuracy of classification. We tested the method on four standard datasets. The experimental results showed that, compared with recent classification methods, the number of weight parameters of the proposed method only accounted for less than 5% of the other methods; however, the classification accuracy was equivalent to or even superior to certain high-performance classification methods.

中文翻译：

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用于遥感场景分类的分支特征融合卷积网络


卷积神经网络（CNN）在遥感场景分类中具有突出的优势。分类性能较好的深度CNN模型通常具有较高的复杂度，而复杂度较低的浅层CNN模型对于具有复杂空间结构的遥感图像很少能取得良好的分类性能。在本文中，我们提出了一种新的基于分支特征融合的轻量级CNN分类方法（LCNN-BFF），用于遥感场景分类。与传统的单线性卷积结构相比，所提出的模型具有双线性特征提取结构。利用BFF方法融合两个分支提取的特征信息，提高了分类精度。此外，将深度可分离卷积与常规卷积相结合来提取图像特征，在保证分类精度的前提下，大大降低了模型的复杂度。我们在四个标准数据集上测试了该方法。实验结果表明，与现有的分类方法相比，该方法的权重参数数量仅占其他方法的不到5%；然而，分类精度相当于甚至优于某些高性能分类方法。