Hyperspectral Image Classification On Casi

Overall Accuracy

Results

Performance results of various models on this benchmark

		Paper Title
SSDGL	95.36	A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification
AMS-M2ESL	88.82±0.93%	Adaptive Mask Sampling and Manifold to Euclidean Subspace Learning with Distance Covariance Representation for Hyperspectral Image Classification
FPGA	86.61	FPGA: Fast Patch-Free Global Learning Framework for Fully End-to-End Hyperspectral Image Classification
CVSSN	82.55±0.47%	Exploring the Relationship between Center and Neighborhoods: Central Vector oriented Self-Similarity Network for Hyperspectral Image Classification

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