Quantum Feature-Empowered Deep Classification for Fast Mangrove Mapping

2025-01-06

IEEE Transactions on Geoscience and Remote Sensing

AI Summary

To address low classification accuracy, poor generalizability, and model redundancy in mangrove remote sensing mapping, this paper proposes a quantum-enhanced lightweight classification framework. Methodologically, we design a novel purely quantum feature extraction branch and interpretable quantum neurons to construct an entanglement-enabled spatial-spectral quantum feature module; a dual-track fusion architecture then enables positive, complementary integration of quantum unitary features with CNN-derived spatial-spectral features at the feature level—avoiding parameter inflation and spurious performance gains. Experiments on multi-source remote sensing datasets demonstrate that our approach significantly improves classification accuracy (+3.2%) and cross-regional generalization capability, while reducing model parameters by 37%. These results empirically validate the substantive contribution of quantum features to land-cover classification and establish an efficient, interpretable quantum-intelligent paradigm for ecological conservation.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

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.

Problem

• Quantum Technology
• Mangrove Mapping
• Environmental Protection

Innovation

• Quantum Enhanced Deep Network
• Mangrove Mapping
• Quantum-Traditional Information Fusion