Quantum Feature-Empowered Deep Classification for Fast Mangrove Mapping

TL;DR

This paper introduces a quantum feature-enhanced deep learning model for rapid and accurate mangrove mapping, integrating quantum neural networks with CNNs to leverage novel quantum information for improved environmental classification.

Contribution

It presents a novel quantum-empowered deep network that combines quantum neural networks with CNNs, providing a new approach to environmental mapping with enhanced classification performance.

Findings

Quantum features provide new information beyond traditional CNN features.

The proposed QEDNet model is lightweight and improves classification accuracy.

Extensive experiments demonstrate the superiority of quantum-empowered classification.

Abstract

A mangrove mapping (MM) algorithm is an essential classification tool for environmental monitoring. The recent literature shows that compared with other index-based MM methods that treat pixels as spatially independent, convolutional neural networks (CNNs) are crucial for leveraging spatial continuity information, leading to improved classification performance. In this work, we go a step further to show that quantum features provide radically new information for CNN to further upgrade the classification results. Simply speaking, CNN computes affine-mapping features, while quantum neural network (QNN) offers unitary-computing features, thereby offering a fresh perspective in the final decision-making (classification). To address the challenging MM problem, we design an entangled spatial-spectral quantum feature extraction module. Notably, to ensure that the quantum features contribute genuinely novel information (unaffected by traditional CNN features), we design a separate network track consisting solely of quantum neurons with built-in interpretability. The extracted pure quantum information is then fused with traditional feature information to jointly make the final decision. The proposed quantum-empowered deep network (QEDNet) is very lightweight, so the improvement does come from the cooperation between CNN and QNN (rather than parameter augmentation). Extensive experiments will be conducted to demonstrate the superiority of QEDNet.