基于空间与光谱注意力的光学图像和SAR图像特征融合分类方法

针对多源遥感图像的差异性和互补性问题，该文提出一种基于空间与光谱注意力的光学图像和SAR图像特征融合分类方法。首先利用卷积神经网络分别进行光学图像和SAR图像的特征提取，设计空间注意力和光谱注意力组成的注意力模块分析特征重要程度，生成不同特征的权重进行特征融合增强，同时减弱对无效信息的关注，从而提高光学和SAR图像融合分类精度。通过在两组光学和SAR图像数据集上进行对比实验，结果表明所提方法取得更高的融合分类精度。

Feature Fusion Classification for Optical Image and SAR Image Based on Spatial-spectral Attention

Considering the issue of difference and complementarity of multi-source remote sensing images, this paper proposes a feature fusion classification method for optical image and SAR image based on spatial-spectral attention. Firstly, features of optical image and SAR image are extracted by the convolutional neural network, and an attention module composed of spatial attention and spectral attention is designed to analyze the importance of features. Features can be enhanced by the weights of the attention module, which can reduce the attention to irrelevant information, and thus improve the accuracy of fusion classification for optical and SAR images. Experimental results on two datasets of optical image and SAR image demonstrate that the proposed method is able to yield higher fusion classification accuracy.