Development of quantum superpixel entanglement model for remote sensing image classification

The existing models based on quantum pixels can mine spectral information and entanglement features, but are time-consuming in practice due to its characteristics on pixel-level entanglement. For this reason, an innovative image classification model, named ‘Quantum Superpixel Entanglement Classification (QSEC) model’, is proposed in this study. By treating superpixels in remote sensing images (RSIs) as quantum particles, we extend the paradigm of image classification from the pixel level to the superpixel level. This design fully leverages the spectral information, textural features, and inter-superpixel quantum entanglement characteristics inherent in RSIs, enabling the effective capture and processing of complex image information via quantum entanglement-driven computational frameworks. Furthermore, the framework quantizes RSI superpixels to map their greyscale and texture features onto corresponding quantum states, constructs a quantum superpixel entanglement pattern, extracts the entanglement relationships between superpixels using entanglement coefficients, and ultimately develops a classification method applicable to multispectral images. The results from the four groups of the experiments demonstrated that: (1) The QSEC model achieves an average classification accuracy of and Kappa coefficient (KC) of 92.02% and 0.86, respectively, with an average running time of 29.25 seconds; (2) the running time of the QSEC model was decreased by up to 400 seconds, the classification speed was improved by 75.29% when compared with the neural network classification (NNC), support vector machine (SVM) and deep learning (DL) model; and (3) the QSEC model improves classification accuracy by 8.76% and increases KC by 0.12 when compared with unsupervised classification models such as ISODATA and K-means.