Gao Yunhao, Li Wei, Wang Junjie, Zhang Mengmeng, Tao Ran
IEEE Trans Image Process. 2024;33:3271-3284. doi: 10.1109/TIP.2024.3394217. Epub 2024 May 8.
Advances in multisource remote sensing have allowed for the development of more comprehensive observation. The adoption of deep convolutional neural networks (CNN) naturally includes spatial-spectral information, which has achieved promising performance in multisource data classification. However, challenges are still found with the extraction of spatial distribution and spectrum relationships, which eventually limit the classification performance. To solve the issue, a spatial-spectral perception network (S2PNet) is proposed to extract the advantages of different data sources and the cross information between data sources in a targeted manner. Specifically, the spatial perception network is developed to build the spatial distribution relationship from high-resolution images, while the spectral perception network extracts the spectrum relationship from spectral images. For perceiving cross information, a memory unit is utilized to store the features from different data sources in succession. In addition, the distance loss and reconstruction loss are introduced to keep the feature integrity, and the cross-entropy loss ensures that features can distinguish different classes. The comprehensive experiments are conducted on several datasets to validate the superiority of the proposed algorithm. The proposed S2PNet outperforms the considered classifiers with an average improvement of +0.77%, +5.62%, +1.58%, and +1.79% for overall accuracy values.
多源遥感技术的进步使得更全面的观测得以发展。深度卷积神经网络（CNN）的采用自然地包含了空间光谱信息，这在多源数据分类中取得了可观的性能。然而，在空间分布和光谱关系的提取方面仍存在挑战，这最终限制了分类性能。为了解决这个问题，提出了一种空间光谱感知网络（S2PNet），以有针对性地提取不同数据源的优势以及数据源之间的交叉信息。具体而言，开发了空间感知网络以从高分辨率图像构建空间分布关系，而光谱感知网络则从光谱图像中提取光谱关系。为了感知交叉信息，利用一个记忆单元依次存储来自不同数据源的特征。此外，引入距离损失和重建损失以保持特征完整性，交叉熵损失确保特征能够区分不同类别。在多个数据集上进行了综合实验，以验证所提算法的优越性。所提出的S2PNet在总体准确率值方面比所考虑的分类器表现更优，平均提升了+0.77%、+5.62%、+1.58%和+1.7
