Duan Sining, Zhao Jingyi, Huang Xinyi, Zhao Shuhe
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources, School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China.
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China.
Sensors (Basel). 2024 Feb 19;24(4):1324. doi: 10.3390/s24041324.
The common channel attention mechanism maps feature statistics to feature weights. However, the effectiveness of this mechanism may not be assured in remotely sensing images due to statistical differences across multiple bands. This paper proposes a novel channel attention mechanism based on feature information called the feature information entropy attention mechanism (FEM). The FEM constructs a relationship between features based on feature information entropy and then maps this relationship to their importance. The Vaihingen dataset and OpenEarthMap dataset are selected for experiments. The proposed method was compared with the squeeze-and-excitation mechanism (SEM), the convolutional block attention mechanism (CBAM), and the frequency channel attention mechanism (FCA). Compared with these three channel attention mechanisms, the mIoU of the FEM in the Vaihingen dataset is improved by 0.90%, 1.10%, and 0.40%, and in the OpenEarthMap dataset, it is improved by 2.30%, 2.20%, and 2.10%, respectively. The proposed channel attention mechanism in this paper shows better performance in remote sensing land use classification.
通用通道注意力机制将特征统计信息映射为特征权重。然而，由于多波段之间的统计差异，这种机制在遥感图像中的有效性可能无法得到保证。本文提出了一种基于特征信息的新型通道注意力机制，称为特征信息熵注意力机制（FEM）。FEM基于特征信息熵构建特征之间的关系，然后将这种关系映射到它们的重要性上。选择了Vaihingen数据集和OpenEarthMap数据集进行实验。将所提出的方法与挤压激励机制（SEM）、卷积块注意力机制（CBAM）和频率通道注意力机制（FCA）进行了比较。与这三种通道注意力机制相比，FEM在Vaihingen数据集中的mIoU分别提高了0.90%、1.10%和0.40%，在OpenEarthMap数据集中分别提高了2.30%、2.20%和2.10%。本文提出的通道注意力机制在遥感土地利用分类中表现出更好的性能。
