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Hyperspectral Image Classification On Pavia

Metriken

AA@5%perclass
Kappa@5%perclass
OA@5%perclass
Overall Accuracy

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Vergleichstabelle
ModellnameAA@5%perclassKappa@5%perclassOA@5%perclassOverall Accuracy
exploring-the-relationship-between-center-and99.52±0.17%0.9957±0.000999.68±0.06%99.68±0.06%
wide-contextual-residual-network-with-active---99.43%
hyperspectral-image-classification-of----
adaptive-mask-sampling-and-manifold-to---98.09±0.30%
attention-based-adaptive-spectral-spatial---99.85
hsi-bert-hyperspectral-image-classification----
shorten-spatial-spectral-rnn-with-parallel---98.44%
deep-metric-learning-based-feature-embedding----
feature-extraction-of-hyperspectral-images----
locality-aware-hyperspectral-classification----
spectralnet-exploring-spatial-spectral---99.99%
fpga-fast-patch-free-global-learning-1---99.81%
hyperspectral-image-classification-via-a----
graph-information-aggregation-cross-domain----
bass-net-band-adaptive-spectral-spatial---97.48%
small-sample-hyperspectral-image----
deep-supervised-learning-for-hyperspectral----
hyperspectral-image-classification-with-1---96.18
faster-hyperspectral-image-classification---99.96%
jigsawhsi-a-network-for-hyperspectral-image---100.00
attention-based-second-order-pooling-network---99.65%
generative-adversarial-networks-based-on-1----
hyperspectral-image-classification-using-deep---99.99%
spectral-spatial-classification-of-2----
a-spectral-spatial-dependent-global-learning---99.97%
deep-learning-for-classification-of-1---96.71
generative-adversarial-networks-based-on-2----