A Novel Iterative Self-Organizing Pixel Matrix Entanglement Classifier for Remote Sensing Imagery

Abstract

The previously presented self-organizing pixel entanglement neural network (SOPENN) model only establishes 2-D basis vectors that are orthogonal to each other in the Hilbert space, which cannot sufficiently reflect the spectral information and the entanglement characteristics of pixels in multispectral images. Therefore, an iterative self-organizing pixel matrix entanglement (ISOPME) image classification model is proposed in this article. Quantum pixel matrix entanglement (PME) is based on quantum pixel entanglement, which considers a pixel as a quantum, and the quantum entanglement theory in quantum informatics is applied in the PME. First, the PME theory was developed to associate the quantum states of pixels with their gray values. Second, the PME coefficient was proposed to extract the entanglement relationship between pixel matrices with 3-D basis vectors in the Hilbert space. Finally, an ISOPME model was developed to implement a self-organizing clustering for multispectral remote sensing image classification. The experimental results for four test areas demonstrate that: 1) the proposed ISOPME approach achieves an average classification accuracy of 92.02% and a Kappa coefficient (KC) of 0.88; 2) when compared with four traditional unsupervised classification methods, ISOPME on average improves the classification accuracy by 8.96% and the KC by 0.14; and 3) the classification accuracy and KC from ISOPME reach the same level as the more sophisticated supervised classification methods, such as support vector machine (SVM), and are close to those obtained using deep learning (DL) classification methods.

Topics & Concepts