Deep Temporal Joint Clustering for Satellite Image Time-Series Analysis

Abstract

With the advancement of remote sensing satellite technology, the acquisition of Satellite Image Time-Series (SITS) data has significantly increased, providing new opportunities and challenges for land cover analysis. Traditional unsupervised clustering methods often struggle with the complexity of these data due to limitations in scalability and generalization capabilities. In response, this paper proposes a new unsupervised learning approach called deep temporal joint clustering (DTJC) designed for efficient pixel-wise clustering of SITS data. DTJC optimizes the reconstruction of temporal information along with clustering objectives, which not only preserves the temporal dynamics of the original data but also creates a feature space conducive to clustering. Experimental results show that DTJC achieves optimal clustering performance across four publicly available multi-spectral SITS datasets, including TimeSen2Crop, Cerrado Biome, Reunion Island, and Imperial datasets. Compared to traditional K-means and projection algorithms, DTJC significantly improves clustering accuracy, especially in environments with complex geographical distributions. Leveraging the principles of the K-means clustering algorithm, DTJC showcases remarkable performance improvements over traditional optimized K-means and projection algorithms in land cover analysis, heralding a new era in the unsupervised learning landscape of SITS data. The DTJC method greatly enhances the efficiency of SITS data analysis without the need for labeled data, making it a powerful tool for automated land cover classification and environmental monitoring.

Keywords

• Clustering
• deep learning
• joint optimization
• satellite image time series (SITS)
• unsupervised