School of Software, Liaoning Technical University, Huludao, Liaoning, China.
The Department of Basic Education, Liaoning Technical University, Huludao, Liaoning, China.
PLoS One. 2024 Aug 28;19(8):e0308789. doi: 10.1371/journal.pone.0308789. eCollection 2024.
Addressing the challenges in effectively extracting multi-scale features and preserving pose information during hyperspectral image (HSI) classification, a Multi-Scale Depthwise Separable Capsule Network (MDSC-Net) is proposed in this article for HSI classification. Initially, hierarchical features are extracted by MDSC-Net through the employment of parallel multi-scale convolutional kernels, while computational complexity is reduced via depthwise separable convolutions, thus reducing the overall computational load and achieving efficient feature extraction. Subsequently, to enhance the translational invariance of features and reduce the loss of pose information, features of various scales are processed in parallel by independent capsule networks, with improvements in max pooling achieved through dynamic routing. Lastly, features of different scales are concatenated and integrated through the concatenate operation, thereby facilitating precise analysis of multi-level information in the hyperspectral image classification process. Experimental comparisons demonstrate that MDSC-Net achieves average accuracies of 94%, 98%, and 99% on the Kennedy Space Center, University of Pavia, and Salinas datasets, respectively, indicating a significant performance advantage over recent HSI classification models and validating the effectiveness of the proposed model.
针对高光谱图像（HSI）分类中有效提取多尺度特征和保留姿态信息的挑战，本文提出了一种用于 HSI 分类的多尺度深度可分离胶囊网络（MDSC-Net）。首先，MDSC-Net 通过使用并行多尺度卷积核提取分层特征，同时通过深度可分离卷积降低计算复杂度，从而降低整体计算负荷并实现高效的特征提取。其次，为了增强特征的平移不变性并减少姿态信息的损失，通过独立的胶囊网络并行处理不同尺度的特征，通过动态路由实现最大池化的改进。最后，通过连接操作将不同尺度的特征进行连接和集成，从而有助于精确分析高光谱图像分类过程中的多层次信息。实验比较表明，MDSC-Net 在肯尼迪航天中心、帕维亚大学和萨利纳斯数据集上的平均准确率分别为 94%、98%和 99%，与最近的 HSI 分类模型相比具有显著的性能优势，验证了所提出模型的有效性。
