A graph-regulated disentangling Mamba model with sparse tokens achieves 93.94% accuracy classifying tree species from MODIS time series in Alberta and outperforms twelve prior models.
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ConvVitMamba integrates multiscale convolution, transformer encoding, and Mamba-based refinement with PCA to outperform prior CNN, ViT, and Mamba methods in accuracy, size, and speed on four HSI benchmark datasets.
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A Novel Graph-Regulated Disentangling Mamba Model with Sparse Tokens for Enhanced Tree Species Classification from MODIS Time Series
A graph-regulated disentangling Mamba model with sparse tokens achieves 93.94% accuracy classifying tree species from MODIS time series in Alberta and outperforms twelve prior models.
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ConvVitMamba: Efficient Multiscale Convolution, Transformer, and Mamba-Based Sequence modelling for Hyperspectral Image Classification
ConvVitMamba integrates multiscale convolution, transformer encoding, and Mamba-based refinement with PCA to outperform prior CNN, ViT, and Mamba methods in accuracy, size, and speed on four HSI benchmark datasets.