Quantum Processing in Fusion of SAR and Optical Images for Deep
Learning: A Data-Centric Approach
Abstract
Deep learning techniques are very prominent in processing remotely
sensed synthetic aperture radar (SAR) images for real-time, high-impact
applications, such as image classification, object detection, and
semantic segmentation. The accuracy of deep learning models, such as
convolutional neural networks (CNNs), depends on the quality of the
input data. Compared to the model-centric approach, where the model
parameters are optimized during training, the data-centric approach can
enhance the performance accuracy as data quality is improved before
training the models. Improving the data quality of SAR images is
challenging as SAR image properties are different from optical (OPT)
images. Image fusion techniques proved to enhance the quality of SAR
images when combined with OPT images. Many fusion techniques exist for
combining SAR and OPT images in the classical domain. This paper
proposes a novel approach to using quantum computing for the image
fusion of SAR and OPT images. Eight different quantum processing
techniques are used for the fusion of the images. We designed and
created a dataset for land-use classification by collecting data using
the Google Earth Engine. The quality metric measurements show that the
quality of SAR images has improved by using the proposed quantum
processing techniques. In addition, performance evaluation of the deep
learning CNNs on the dataset was carried out for all quantum processing
techniques. Our approach improved the classification accuracy from
82.64%, with only SAR images for training, to 95.36% using the
proposed image fusion techniques.