Patent AU-2021102646-A4 (AU)
Title: Automated hyperspectral image clustering using multilevel quantum differential evolution on quantum hardware
Inventor: DUTTA TULIKA
IPC: G06T7/168
Filed: 2021-05-18
Granted: 2021-07-08
Abstract: AUTOMATED HYPERSPECTRAL IMAGE CLUSTERING USING MULTILEVEL 
QUANTUM DIFFERENTIAL EVOLUTION ON QUANTUM HARDWARE 
Abstract: 
Hyperspectral images are data cubes composed of huge spectral information. The spectral 
bands contain abundant information but are also full of redundant data. The huge information 
content also increases the space and time complexity to deal with hyperspectral images and 
due to Hughes phenomena, the accuracy also decreases with increase in information content.  
The constraint of research data and ground truth images of hyperspectral images is a real 
limitation of efficiently developing algorithms, especially supervised ones which require 
priori knowledge about the dataset. Unsupervised approaches are thus more preferred while 
working with hyperspectral images. In our proposed architecture a hybrid quantum system is 
developed. The proposed architecture consists of spectral band selection and automatic 
clustering of selected bands. For band selection an unsupervised three-Dimensional Discrete 
Cosine transform and weighted Shannon entropy is used. The most informative bands are 
chosen using weighted dimensional discrete cosine transform. Multilevel quantum 
Differential Evolution (DE) is designed using a hybrid quantum architecture which provides 
better optimized results and speedy execution. Different quantum gates are used for achieving 
quantum superposition, population maintenance and producing future generations in the DE 
algorithm. The automatic clustering methods solves the constraint of dealing with 
hyperspectral data in absence of ground truth images. The quantum population used in the 
DE algorithm is divided into two parts to maintain a balanced exploration and exploitation 
mechanism. Moreover, quantum gates are used to employ quantum disaster operation so that 
the population does not get stuck in local minima. The quantum population initialization and 
modifications are done on real quantum machines whereas rest of the process where huge 
calculations are required are implemented on classical computer. Existing methods used for 
processing hyperspectral data are either supervised or semi-supervised processes. This 
architecture is proposed so that both spectral and spatial information can be segmented 
efficiently in an unsupervised manner from a hyperspectral image. Existing methods mostly 
work on classical metaheuristic algorithms or quantum inspired metaheuristics. In this work a 
hybrid quantum algorithm has been developed which helps in actually getting the quantum 
supremacy.  
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Spectral Band Selection 
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Fig 1. Spectral Segmentation Architecture 
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Fig 2. Spatial Segmentation Architecture 
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