HyperAI초신경
홈뉴스최신 연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
전체 검색
소개
HyperAI초신경

Hyperspectral Image Classification On Pavia

평가 지표

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

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
AA@5%perclass
Kappa@5%perclass
OA@5%perclass
Overall Accuracy
Paper TitleRepository
CVSSN99.52±0.17%0.9957±0.000999.68±0.06%99.68±0.06%Exploring the Relationship between Center and Neighborhoods: Central Vector oriented Self-Similarity Network for Hyperspectral Image Classification
WCRN---99.43%Wide Contextual Residual Network with Active Learning for Remote Sensing Image Classification
3D VS-CNN----Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples-
AMS-M2ESL---98.09±0.30%Adaptive Mask Sampling and Manifold to Euclidean Subspace Learning with Distance Covariance Representation for Hyperspectral Image Classification
A2S2K-ResNet---99.85Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification
HSI-BERT----HSI-BERT: Hyperspectral Image Classification Using the Bidirectional Encoder Representation From Transformers-
St-SS-pGRU---98.44%Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image Classification
S-DMM----Deep Metric Learning-Based Feature Embedding for Hyperspectral Image Classification-
IFRF----Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering-
HyLITE----Locality-Aware Hyperspectral Classification
SpectralNET---99.99%SpectralNET: Exploring Spatial-Spectral WaveletCNN for Hyperspectral Image Classification
FPGA---99.81%FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification-
RPNet----Hyperspectral image classification via a random patches network
DCFSL----Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image Classification
BASSNet---97.48%BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification
RPNet-RF----Small Sample Hyperspectral Image Classification Based on the Random Patches Network and Recursive Filtering
2D-CNN----Deep supervised learning for hyperspectral data classification through convolutional neural networks
CNN-MRF---96.18Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network
FSKNet---99.96%Faster hyperspectral image classification based on selective kernel mechanism using deep convolutional networks
JigsawHSI---100.00JigsawHSI: a network for Hyperspectral Image classification
0 of 27 row(s) selected.