A Novel Semi-Supervised Multi-View Picture Fuzzy Clustering Approach for Enhanced Satellite Image Segmentation

Satellite image segmentation plays a crucial role in remote sensing, supporting applications such as environmental monitoring, land use analysis, and disaster management. However, traditional segmentation methods often rely on large amounts of labeled data, which are costly and time-consuming to obtain, especially in large-scale or dynamic environments. To address this challenge, we propose the Semi-Supervised Multi-View Picture Fuzzy Clustering (SS-MPFC) algorithm, which improves segmentation accuracy and robustness, particularly in complex and uncertain remote sensing scenarios. SS-MPFC unifies three paradigms: semi-supervised learning, multi-view clustering, and picture fuzzy set theory. This integration allows the model to effectively utilize a small number of labeled samples, fuse complementary information from multiple data views, and handle the ambiguity and uncertainty inherent in satellite imagery. We design a novel objective function that jointly incorporates picture fuzzy membership functions across multiple views of the data, and embeds pairwise semi-supervised constraints (must-link and cannot-link) directly into the clustering process to enhance segmentation accuracy. Experiments conducted on several benchmark satellite datasets demonstrate that SS-MPFC significantly outperforms existing state-of-the-art methods in segmentation accuracy, noise robustness, and semantic interpretability. On the Augsburg dataset, SS-MPFC achieves a Purity of 0.8158 and an Accuracy of 0.6860, highlighting its outstanding robustness and efficiency. These results demonstrate that SS-MPFC offers a scalable and effective solution for real-world satellite-based monitoring systems, particularly in scenarios where rapid annotation is infeasible, such as wildfire tracking, agricultural monitoring, and dynamic urban mapping.