Zhang Yongshan, Yan Shuaikang, Zhang Lefei, Du Bo
IEEE Trans Image Process. 2024;33:4640-4653. doi: 10.1109/TIP.2024.3444323. Epub 2024 Aug 28.
Multimodal remote sensing image recognition is a popular research topic in the field of remote sensing. This recognition task is mostly solved by supervised learning methods that heavily rely on manually labeled data. When the labels are absent, the recognition is challenging for the large data size, complex land-cover distribution and large modality spectrum variation. In this paper, a novel unsupervised method, named fast projected fuzzy clustering with anchor guidance (FPFC), is proposed for multimodal remote sensing imagery. Specifically, according to the spatial distribution of land covers, meaningful superpixels are obtained for denoising and generating high-quality anchor. The denoised data and anchors are projected into the optimal subspace to jointly learn the shared anchor graph as well as the shared anchor membership matrix from different modalities in an adaptively weighted manner to accelerate the clustering process. Finally, the shared anchor graph and shared anchor membership matrix are combined to derive clustering labels for all pixels. An effective alternating optimization algorithm is designed to solve the proposed formulation. This is the first attempt to propose a soft clustering method for large-scale multimodal remote sensing data. Experiments show that the proposed FPFC achieves 81.34%, 55.43% and 93.34% clustering accuracies on the three datasets and outperforms the state-of-the-art methods. The source code is released at https://github.com/ZhangYongshan/FPFC.
多模态遥感图像识别是遥感领域一个热门的研究课题。这项识别任务大多通过严重依赖人工标注数据的监督学习方法来解决。当没有标签时，由于数据量巨大、土地覆盖分布复杂以及模态光谱变化大，识别工作具有挑战性。本文针对多模态遥感影像提出了一种名为带锚点引导的快速投影模糊聚类（FPFC）的新型无监督方法。具体而言，根据土地覆盖的空间分布，获取有意义的超像素用于去噪并生成高质量的锚点。将去噪后的数据和锚点投影到最优子空间，以自适应加权的方式联合学习来自不同模态的共享锚点图以及共享锚点隶属矩阵，从而加速聚类过程。最后，将共享锚点图和共享锚点隶属矩阵相结合，为所有像素导出聚类标签。设计了一种有效的交替优化算法来求解所提出的公式。这是首次尝试为大规模多模态遥感数据提出一种软聚类方法。实验表明，所提出的FPFC在三个数据集上的聚类准确率分别达到81.34%、55.43%和93.34%，优于现有方法。源代码已发布在https://github.com/ZhangYongshan/FPFC 。
