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Hyperspectral Semantic Segmentation 벤치마크

Hyperspectral Semantic Segmentation on HSI-Drive v2.0

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Accuracy

92.51 93.4 94.3 95.19 96.08 2024-09 2026-09 RU-Net — 96.08 (2024-09-17) U-Net — 94.95 (2024-09-17) DeepLabV3+ — 92.51 (2024-09-17) RU-Net — 96.08 (2024-09-17) U-Net — 94.95 (2024-09-17) DeepLabV3+ — 92.51 (2024-09-17) RU-Net — 96.08 (2024-09-17)
RankModel AccuracyAverage AccuracyAvg. F1Jaccard (Mean) PaperCodeYear
1 RU-Net 96.0879.8282.3472.18 HS3-Bench: A Benchmark and Strong Baseline for Hyperspectral Semantic Segmentation in Driving Scenarios nickstheisen/hyperseg 2024
2 U-Net 94.9574.7476.0864.95 HS3-Bench: A Benchmark and Strong Baseline for Hyperspectral Semantic Segmentation in Driving Scenarios nickstheisen/hyperseg 2024
3 DeepLabV3+ 92.5165.5867.8656.63 HS3-Bench: A Benchmark and Strong Baseline for Hyperspectral Semantic Segmentation in Driving Scenarios nickstheisen/hyperseg 2024
4 RU-Net 96.0879.8282.3472.18 HS3-Bench: A Benchmark and Strong Baseline for Hyperspectral Semantic Segmentation in Driving Scenarios nickstheisen/hyperseg 2024
5 U-Net 94.9574.7476.0864.95 HS3-Bench: A Benchmark and Strong Baseline for Hyperspectral Semantic Segmentation in Driving Scenarios nickstheisen/hyperseg 2024
6 DeepLabV3+ 92.5165.5867.8656.63 HS3-Bench: A Benchmark and Strong Baseline for Hyperspectral Semantic Segmentation in Driving Scenarios nickstheisen/hyperseg 2024
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