Binary Classification of Remotely Sensed Images Using SVD Based GLCM Features in Quantum Framework

Texture feature extraction is very important in landuse land cover(LULC) classification of satellite images. Through this paper, a new method for texture feature extraction is presented which uses singular value decomposition(SVD) and gray level cooccurance matrix (GLCM). Here we test for capabilities of the singular values thus generated for classification of textures. We also try to find out if these singular values can be used as a substitute for Haralick texture features. In this proposed method sample images are multi-thresholded to reduce the dimensionality. GLCM is generated for each image-patch after applying the thresholds. Later, the SVD decomposition of GLCM provides singular values which are used as a feature vector for classification of the image patches. We evaluated our proposed technique based on three criteria a) images from totally different classes b) images from same class but with different textures c) images from same class, same texture but different orientation. We classified the images using minimum distance to mean(MDM), SVM using radial bias kernel (cSVM) and SVM using quantum kernel (qSVM). We used IBM gate-based qiskit to generate a quantum kernel and classify using qSVM. From the experiment we conclude that singluar values are good and stable as textures features and greatly enhance the texture classification. Singular Values along with quantum kernel have produced very promising results.