LegoQ: Density-Matrix Representation Learning with Spectral-Spatial State Transitions for Hyperspectral Classification
Quick Answer
The paper introduces LegoQ, a density-matrix representation learning framework for hyperspectral image classification, achieving 96.20% accuracy on Indian Pines and 97.52% on WHU-Hi-LongKou.
Quick Take
It effectively addresses mixed pixels and spectral ambiguity without requiring quantum hardware, offering improved diagnostics through sample-level metrics.
Key Points
- LegoQ uses a classical density-matrix framework for hyperspectral image classification.
- Achieved 96.20% accuracy on Indian Pines and 97.52% on WHU-Hi-LongKou.
- Aggregates group states and compares them with learnable class-prototype density matrices.
- Offers improved diagnostics like von Neumann entropy and prototype fidelity.
- Provides a practical alternative to vector-only hyperspectral classification.
Paper Resources
Source Excerpt
Hyperspectral image classification is complicated by mixed pixels, spectral ambiguity, class imbalance, and limited annotations. Most current classifiers encode a pixel or patch as a deterministic vector and apply a linear or multilayer softmax head. Although effective for discrimination, this representation does not directly expose how mixed or uncertain a sample is. This paper presents \method, a classical density-matrix representation learning framework for hyperspectral images. The spectral
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