This is a keras reprodction of TRGS paper:Spectral–Spatial Residual Network for Hyperspectral Image Classification: A 3-D Deep Learning Framework
Test loss: 0.14532101154327393
Test acc: 95.90243697166443%
Classification result:
precision recall f1-score support
Alfalfa 1.00 0.50 0.67 32
Corn-notill 0.96 0.92 0.94 1000
Corn-mintill 0.96 0.98 0.97 581
Corn 0.95 0.99 0.97 166
Grass-pasture 0.96 0.96 0.96 338
Grass-trees 1.00 0.98 0.99 511
Grass-pasture-mowed 1.00 0.47 0.64 19
Hay-windrowed 0.92 1.00 0.96 334
Oats 1.00 0.93 0.96 14
Soybean-notill 0.93 0.94 0.94 681
Soybean-mintill 0.98 0.97 0.97 1719
Soybean-clean 0.92 0.96 0.94 416
Wheat 1.00 0.99 0.99 143
Woods 0.97 0.98 0.97 886
Building-Gras-Tree-Drive 0.91 0.91 0.91 270
Stone-Steel-Towers 0.97 1.00 0.98 65
accuracy 0.96 7175
macro avg 0.96 0.90 0.92 7175
weighted avg 0.96 0.96 0.96 7175
Confusion matrix:
Predict map:
