Research on quantum many-body entanglement image classification

Remote sensing image (RSI) classification is key for extracting value from remote sensing data, aiding decision-making and resource management. Recent quantum pixel-based models excel at handling complex spectral and entanglement features but face high computational costs and lengthy processing due to quantum pixel entanglement, limiting large-scale and real-time applications. To address this, this study proposes a novel Quantum Many-Body Entanglement Classification model. This model first performs superpixel segmentation on RSIs, and then converts the segmented images into quantum states with quantum characteristics. Each superpixel corresponds to one quantum state, thereby enhancing the classification granularity from the pixel level to the superpixel level. Subsequently, the model conducts cluster analysis based on the entanglement coefficient. This paper takes Dongying, Shandong Province as the research area and uses Landsat 8 image data to conduct a comparative experiment between the proposed classification method and the traditional remote sensing classification method. The results show that the classification accuracy of this method meets the requirements of practical applications.