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  4. Vol 9, Issue 3, 2025

Multi-Scale Graph Wavelet Convolution for Hyperspectral-LiDAR Urban Scene Classification

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DOI: 10.23977/acss.2025.090311 | Downloads: 15 | Views: 320

Author(s)

Junhua Ku 1,2, Jie Zhao 3

Affiliation(s)

1 School of Information Science and Technology, Qiongtai Normal University, Haikou, Hainan, 571127, China
2 Institute of Educational Big Data and Artificial Intelligence, Qiongtai Normal University, Haikou, Hainan, 571127, China
3 School of Science, Qiongtai Normal University, Haikou, Hainan, 571127, China

Corresponding Author

Junhua Ku

ABSTRACT

The Houston 2013 benchmark conducted in Houston describes a methodology that constructs a sparse pixel graph exclusively over labeled pixels. This approach employs learnable multi-scale spectral filtering using Chebyshev-approximated graph wavelets and incorporates a lightweight Multi-Layer Perceptron (MLP) head for classification purposes. The training procedure integrates feature MixUp at labeled nodes, a composite loss function combining focal loss and label smoothing, exponential moving average (EMA) weight tracking, an AdamW optimizer with warm-up and cosine scheduling, and mild graph augmentation techniques such as random edge drop and addition with re-normalization. Implementation executed precisely in accordance with the original methodology, with five independent runs producing an overall accuracy (OA) of 90.99% ± 0.41%, average accuracy (AA) of 92.11% ± 0.35%, and a Kappa coefficient (κ) of 0.9022 ± 0.0044. These results demonstrate not only high accuracy but also minimal variability, providing reassurance of the robustness of the methodology.

KEYWORDS

Hyperspectral imaging; LiDAR; graph neural networks; graph wavelets; multi-sensor fusion; urban land cover; Chebyshev approximation; focal loss; label smoothing

CITE THIS PAPER

Junhua Ku, Jie Zhao, Multi-Scale Graph Wavelet Convolution for Hyperspectral-LiDAR Urban Scene Classification. Advances in Computer, Signals and Systems (2025) Vol. 9: 88-97. DOI: http://dx.doi.org/10.23977/acss.2025.090311.

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