Wu Song, Cao Jian-Min, Zhao Xin-Yu
Jilin Agricultural University, Changchun, China.
PLoS One. 2025 Feb 6;20(2):e0316830. doi: 10.1371/journal.pone.0316830. eCollection 2025.
Applying unsupervised classification techniques on remote sensing images enables rapid land cover classification. Using remote sensing imagery from the ZY1-02D satellite's VNIC and AHSI cameras as the basis, multi-source feature information encompassing spectral, edge shape, and texture features was extracted as the data source. The Lanczos algorithm, which determines the largest eigenpairs of a high-order matrix, was integrated with the spectral clustering algorithm to solve for eigenvalues and eigenvectors. The results indicate that this method can quickly and effectively classify land cover. The classification accuracy was significantly improved by incorporating multi-source feature information, with a kappa coefficient reaching 0.846. Compared to traditional classification methods, the improved spectral clustering algorithm demonstrated better adaptability to data distribution and superior clustering performance. This suggests that the method has strong recognition capabilities for pixels with complex spatial shapes, making it a high-performance, unsupervised classification approach.
将无监督分类技术应用于遥感图像可实现快速土地覆盖分类。以ZY1-02D卫星的VNIC和AHSI相机的遥感影像为基础，提取了包含光谱、边缘形状和纹理特征的多源特征信息作为数据源。将用于确定高阶矩阵最大特征对的Lanczos算法与光谱聚类算法相结合，求解特征值和特征向量。结果表明，该方法能够快速有效地对土地覆盖进行分类。通过纳入多源特征信息，分类精度显著提高，kappa系数达到0.846。与传统分类方法相比，改进后的光谱聚类算法对数据分布具有更好的适应性和卓越的聚类性能。这表明该方法对具有复杂空间形状的像素具有很强的识别能力，是一种高性能的无监督分类方法。
