Hyperspectral Image Classification On Indian

Metrics

Overall Accuracy

Results

Performance results of various models on this benchmark

Model Name	Overall Accuracy	Paper Title	Repository
A2S2K-ResNet	99.57 %	Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification
JigsawHSI	99.74	JigsawHSI: a network for Hyperspectral Image classification
Recurrent 3D-CNN	99.50%	Hyperspectral Image Classification with Deep Metric Learning and Conditional Random Field	-
SSBC	-	Discrete Cosine Transform-Based Joint Spectral-Spatial Information Compression and Band Correlation Calculation for Hyperspectral Feature Extraction
CA-GAN	-	Generative Adversarial Networks Based on Collaborative Learning and Attention Mechanism for Hyperspectral Image Classification	-
3D-CNN	-	Spectral–Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network
BASSNet	96.77%	BASS Net: Band-Adaptive Spectral-Spatial Feature Learning Neural Network for Hyperspectral Image Classification
3D VS-CNN	-	Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples	-
S-DMM	-	Deep Metric Learning-Based Feature Embedding for Hyperspectral Image Classification	-
2D-CNN	-	Deep supervised learning for hyperspectral data classification through convolutional neural networks
CNN-MRF	96.12%	Hyperspectral Image Classification with Markov Random Fields and a Convolutional Neural Network
SpectralNET	99.86%	SpectralNET: Exploring Spatial-Spectral WaveletCNN for Hyperspectral Image Classification
TC-GAN	-	Generative Adversarial Networks Based on Transformer Encoder and Convolution Block for Hyperspectral Image Classification	-
RPNet-RF	-	Small Sample Hyperspectral Image Classification Based on the Random Patches Network and Recursive Filtering
HybridSN	99.81%	HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification
IFRF	-	Feature Extraction of Hyperspectral Images With Image Fusion and Recursive Filtering	-
FSKNet	99.83%	Faster hyperspectral image classification based on selective kernel mechanism using deep convolutional networks
HyLITE	89.80	Locality-Aware Hyperspectral Classification
St-SS-pGRU	90.35%	Shorten Spatial-spectral RNN with Parallel-GRU for Hyperspectral Image Classification
DCFSL	-	Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image Classification

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