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.

Multi-view clustering is a critical research area in computer science aimed at effectively extracting meaningful patterns from complex, high-dimensional data that single-view methods cannot capture. Traditional fuzzy clustering techniques, such as Fuzzy C-Means (FCM), face significant challenges in handling uncertainty and the dependencies between different views. To overcome these limitations, we introduce a new multi-view fuzzy clustering approach that integrates picture fuzzy sets with a dual-anchor graph method for multi-view data, aiming to enhance clustering accuracy and robustness, termed Multi-view Picture Fuzzy Clustering (MPFC). In particular, the picture fuzzy set theory extends the capability to represent uncertainty by modeling three membership levels: membership degrees, neutral degrees, and refusal degrees. This allows for a more flexible representation of uncertain and conflicting data than traditional fuzzy models. Meanwhile, dual-anchor graphs exploit the similarity relationships between data points and integrate information across views. This combination improves stability, scalability, and robustness when handling noisy and heterogeneous data. Experimental results on several benchmark datasets demonstrate significant improvements in clustering accuracy and efficiency, outperforming traditional methods. Specifically, the MPFC algorithm demonstrates outstanding clustering performance on a variety of datasets, attaining a Purity (PUR) score of 0.6440 and an Accuracy (ACC) score of 0.6213 for the 3Sources dataset, underscoring its robustness and efficiency. The proposed approach significantly contributes to fields such as pattern recognition, multi-view relational data analysis, and large-scale clustering problems. Future work will focus on extending the method for semi-supervised multi-view clustering, aiming to enhance adaptability, scalability, and performance in real-world applications.