高光谱图像分类论文复现--HybridSNNet

高光谱图像分类–HybridSNNet

论文名：HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification

复现效果：
IndianPines train_size=0.1 1024个训练样本

optimizer’s state_dict:
‘lr’: 0.0005, ‘momentum’: 0.9
epoch:0/100 iter:7/8 loss:2.460165
epoch:1/100 iter:7/8 loss:2.176685
epoch:2/100 iter:7/8 loss:1.679265
epoch:3/100 iter:7/8 loss:1.689592
epoch:4/100 iter:7/8 loss:0.913613
val …
val_acc: 0.609375
epoch:5/100 iter:7/8 loss:0.747501
epoch:6/100 iter:7/8 loss:0.729928
epoch:7/100 iter:7/8 loss:0.681291
epoch:8/100 iter:7/8 loss:0.294558
epoch:9/100 iter:7/8 loss:0.230140
val …
val_acc: 0.9285714285714286
…
epoch:95/100 iter:7/8 loss:0.001309
epoch:96/100 iter:7/8 loss:0.000566
epoch:97/100 iter:7/8 loss:0.001122
epoch:98/100 iter:7/8 loss:0.001579
epoch:99/100 iter:7/8 loss:0.000280
val …
val_acc: 0.9776785714285714

test …
test_acc: 0.9766710069444444

metrics : -------------------------
classify_report :
precision recall f1-score support

     0.0       1.00      0.88      0.94        41
     1.0       0.96      0.96      0.96      1283
     2.0       0.97      0.99      0.98       746
     3.0       1.00      0.98      0.99       213
     4.0       0.97      0.97      0.97       434
     5.0       0.99      0.99      0.99       657
     6.0       1.00      1.00      1.00        25
     7.0       1.00      1.00      1.00       430
     8.0       1.00      0.61      0.76        18
     9.0       0.95      0.99      0.97       875
    10.0       0.99      0.98      0.98      2209
    11.0       0.91      0.97      0.94       533
    12.0       1.00      0.99      1.00       184
    13.0       0.99      0.97      0.98      1137
    14.0       0.99      0.98      0.99       347
    15.0       0.99      0.81      0.89        84

accuracy                           0.98      9216

macro avg 0.98 0.94 0.96 9216
weighted avg 0.98 0.98 0.98 9216

confusion_matrix :
[[ 36 4 0 0 0 0 0 0 0 1 0 0 0 0 0 0]
[ 0 1234 9 0 0 1 0 0 0 3 5 30 0 1 0 0]
[ 0 5 737 0 0 0 0 0 0 3 0 1 0 0 0 0]
[ 0 0 2 209 0 0 0 0 0 0 2 0 0 0 0 0]
[ 0 3 4 0 423 1 0 0 0 3 0 0 0 0 0 0]
[ 0 3 0 0 0 653 0 0 0 0 1 0 0 0 0 0]
[ 0 0 0 0 0 0 25 0 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 0 0 430 0 0 0 0 0 0 0 0]
[ 0 0 0 0 0 1 0 0 11 0 0 6 0 0 0 0]
[ 0 0 1 0 0 0 0 0 0 870 0 1 0 3 0 0]
[ 0 11 0 0 1 3 0 0 0 29 2158 0 0 7 0 0]
[ 0 6 2 0 0 0 0 0 0 3 3 517 0 0 2 0]
[ 0 0 0 0 1 0 0 0 0 0 0 0 183 0 0 0]
[ 0 15 0 0 8 0 0 0 0 2 4 0 0 1107 0 1]
[ 0 0 0 1 2 0 0 0 0 2 2 0 0 0 340 0]
[ 0 0 1 0 0 0 0 0 0 0 0 15 0 0 0 68]]

acc_for_each_class :
[1.0 0.96330991 0.97486772 0.9952381 0.97241379 .9908953 1.0 1. 0 1.0 0.94978166 0.99218391 0.90701754 1. 0.990161 0.99415205 0.98550725]

overall_accuracy: 0.976671
average_accuracy: 0.982221
kappa: 0.9734157229823452