Texture Based LULC Classification of Images Using QSVM

Quantum Machine Learning (QML) is a new interdisciplinary branch that combines quantum computing with machine learning. It is emerging as an alternative to classical machine learning which exploits the quantum mechanical properties of entanglement and superposition to express the hidden patterns in the data. This reduces computational resources as well as the time required for processing. In this study, we tried to address the challenge of landuse land cover classification for classes with similar spectral signatures and small texture dissimilarity. Totally 6 textural features are extracted from a multispectral image using grey level co-occurrence matrix applied on 1st PC component to classify images into the sub-classes(residential, highway, and industrial) of the main built-up class. We used IBM gate-based qiskit to generate a quantum kernel and classify the images using QSVC. Quantum kernels are very expressive when compared to their classical counterparts and can learn complex data more efficiently. The overall accuracy of classification by QSVC is comparable to that of the classical SVC. We summarize our results by saying that QSVC performs better than SVC.