DE-CFFN modifies an existing dual-stream CFFN architecture with Factor Analysis dimensionality reduction and halved 3D convolution filters plus an SE block to match original accuracy at lower complexity on Pavia University and Salinas datasets.
An examination on the effect of CVNN parameters while classifying the real-valued balanced and unbalanced data , url =
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Data Efficient Complex Feature Fusion Network For Hyperspectral Image Classification
DE-CFFN modifies an existing dual-stream CFFN architecture with Factor Analysis dimensionality reduction and halved 3D convolution filters plus an SE block to match original accuracy at lower complexity on Pavia University and Salinas datasets.