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xdu-jjgs / HSI_domain_adaptation

HSI域自适应
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HSI域自适应

目录

数据集描述

Houston数据集

类别 名称 Houston13 Houston18
1 Grass healthy 345 1353
2 Grass stressed 365 4888
3 Trees 365 2766
4 Water 285 22
5 Residential buildings 319 5347
6 Non-residential buildings 408 32459
7 Road 443 6365
total total 2530 53200
shape N H C 210 954 48 210 954 48

HyRANK数据集

类别 名称 Dioni Loukia
1 Dense urban fabric 1262 288
2 Mineral extraction sites 204 67
3 Non irrigated land 614 542
4 Fruit trees 150 79
5 Olive Groves 1768 1401
6 Coniferous Forest 361 500
7 Dense Vegetation 5035 3793
8 Sparce Vegetation 6374 2803
9 Sparce Areas 1754 404
10 Rocks and Sand 492 487
11 Water 1612 1393
12 Coastal Water 398 451
total total 20024 12208
shape N H C 250 1376 176 249 945 176

RGB bands: 23, 11, 07

ShanghaiHangzhou数据集

类别 名称 Hangzhou Shanghai
1 Water 18043 123123
2 Land/Building 77450 161689
3 Plant 40207 83188
total total 135700 368000
shape N H C 1600 260 198 590 230 198

Pavia数据集

类别 名称 PaviaU PaviaC
1 Tree 3064 7598
2 Asphalt 6631 9248
3 Brick 3682 2685
4 Bitumen 1330 7287
5 Shadow 947 2863
6 Meadow 18649 3090
7 Bare soil 5029 6584
total total 39332 39355
shape N H C 610 340 102 1096 715 102

Indiana数据集

类别 名称 Source Target
1 Concrete/ Asphalt 4867 2942
2 Corn cleanTill 9822 6029
3 Corn cleanTill EW 11414 7999
4 Orchard 5106 1562
5 Soybeans cleanTill 4731 4792
6 Soybeans cleanTill EW 2996 1638
7 Wheat 3223 10739
total total 42159 35701
shape N H C 300 400 200 300 400 200

支持的模型

用法

训练

  1. 运行 train/[model]/[dataset].bat文件

  2. 或者运行如下命令

    python train/ddc/train.py configs/houston/dan_1800_average.yaml ^
    --path ./runs/houston/dan-train ^
    --nodes 1 ^
    --gpus 1 ^
    --rank-node 0 ^
    --backend gloo ^
    --master-ip localhost ^
    --master-port 8886 ^
    --seed 30 ^
    --opt-level O2

测试

验证集等于测试集，无需再另行测试

结果

Dataset Model OA-best backbone sample-num sample-order loss loss-ratio kernel batch-size
Houston DNN 0.686±0.035 fe - - softmax+ce 1 - 64
Houston DDC 0.705±0.027 fe - - softmax+ce, mmd loss 1:1 g1 64
Houston DAN 0.694±0.048 fe - - softmax+ce, mmd loss 1:1 g5 64
Houston JAN 0.694±0.033 fe - - softmax+ce, joint mmd loss 1:1 g5 64
Houston DSAN 0.664±0.108 fe - - softmax+ce, local mmd loss 1:1 g5 64
Houston DANN 0.620±0.060 fe - - softmax+ce 1 - 64
Houston MCD 0.632±0.033 fe - - softmax+ce, l1 loss 1:1 - 64
Houston Self-training 0.652±0.003 fe - softmax+ce, cbst loss 1:1 - 100
Houston DST 0.597±0.018 fe - - softmax+ce, wcec loss, cbst loss 1:1:1 - 100
Houston DADST 0.593±0.013 fe - - softmax+ce, wcec loss, adv loss, cbst loss 1:1:1:1 - 100
Houston TSTNet 0.762 fe - - softmax+ce, mmd loss, got loss 1:1:0.1 g5 100
Houston DNN 0.671±0.042 fe 1260 average softmax+ce 1 - 64
Houston DDC 0.676±0.053 fe 1260 average softmax+ce, mmd loss 1:1 g1 64
Houston DAN 0.686±0.058 fe 1260 average softmax+ce, mmd loss 1:1 g5 64
Houston JAN 0.677±0.058 fe 1260 average softmax+ce, joint mmd loss 1:1 g5 64
Houston DSAN 0.643±0.050 fe 1260 average softmax+ce, local mmd loss 1:1 g5 64
Houston DANN 0.590±0.060 fe 1260 average softmax+ce 1 - 64
Houston MCD 0.618±0.027 fe 1260 average softmax+ce, l1 loss 1:1 - 64
Houston Self-training 0.631±0.011 fe 1260 average softmax+ce, cbst loss 1:1 - 64
Houston DST 0.576±0.015 fe 1260 average softmax+ce, wcec loss, cbst loss 1:1:1 - 64
Houston DADST 0.575±0.015 fe 1260 average softmax+ce, wcec loss, adv loss, cbst loss 1:1:1:1 - 64
HyRANK DNN 0.507±0.023 fe - - softmax+ce 1 l 64
HyRANK DDC 0.523±0.030 fe - - softmax+ce, mmd loss 1:1 g1 64
HyRANK DAN 0.504±0.039 fe - - softmax+ce, mmd loss 1:3 g5 64
HyRANK JAN 0.516±0.026 fe - - softmax+ce, joint mmd loss 1:1 g5 64
HyRANK DANN 0.582±0.038 fe - - softmax+ce 1 - 64
HyRANK MCD 0.561±0.026 fe - - softmax+ce, l1 loss 1:1 - 64
HyRANK Self-training 0.514±0.009 fe - - softmax+ce, cbst loss 1:1 - 64
HyRANK DST 0.558±0.021 fe - - softmax+ce, wcec loss, cbst loss 1:1:1 - 64
HyRANK DADST 0.558±0.015 fe - - softmax+ce, wcec loss, adv loss, cbst loss 1:1:1:1 - 64
HyRANK DADST 0.572±0.023 fe - - softmax+ce, wcec loss, adv loss, cbst loss 1:1:2:1 - 64
HyRANK TSTNet 0.633 fe - - softmax+ce, mmd loss, got loss 1:1:0.1 l 100
HyRANK DNN 0.492±0.029 fe 1800 average softmax+ce 1 l 64
HyRANK DDC 0.491±0.028 fe 1800 average softmax+ce, mmd loss 1:1 g1 64
HyRANK DAN 0.496±0.021 fe 1800 average softmax+ce, mmd loss 1:3 g5 64
HyRANK JAN 0.485±0.022 fe 1800 average softmax+ce, joint mmd loss 1:1 g5 64
HyRANK DANN 0.473±0.036 fe 1800 average softmax+ce 1 - 64
HyRANK MCD 0.552±0.027 fe 1800 average softmax+ce, l1 loss 1:1 - 64
HyRANK Self-training 0.514±0.006 fe 1800 average softmax+ce, cbst loss 1:1 - 64
HyRANK DST 0.478±0.034 fe 1800 average softmax+ce, wcec loss, cbst loss 1:1:1 - 64
HyRANK DADST 0.478±0.028 fe 1800 average softmax+ce, wcec loss, adv loss, cbst loss 1:1:1:1 - 64
ShanghaiHangzhou DNN 0.909±0.002 fe - - softmax+ce 1 - 64
ShanghaiHangzhou DDC 0.887±0.008 fe - - softmax+ce, mmd loss 1:1 g1 64
ShanghaiHangzhou DAN 0.904±0.011 fe - - softmax+ce, mmd loss 1:1 g5 64
ShanghaiHangzhou JAN 0.903±0.011 fe - - softmax+ce, joint mmd loss 1:1 g5 64
ShanghaiHangzhou DSAN 0.907±0.005 fe - - softmax+ce, local mmd loss 1:1 g5 64
ShanghaiHangzhou DANN 0.905±0.016 fe - - softmax+ce 1 - 64
ShanghaiHangzhou MCD 0.717±0.105 fe - - softmax+ce, l1 loss 1:1 - 64
ShanghaiHangzhou Self-training 0.915±0.000 fe - - softmax+ce, cbst loss 1:1 - 64
ShanghaiHangzhou DST 0.933±0.012 fe - - softmax+ce, wcec loss, cbst loss 1:1:1 - 64
ShanghaiHangzhou DADST 0.927±0.007 fe - - softmax+ce, wcec loss, adv loss, cbst loss 1:1:1:1 - 64
ShanghaiHangzhou TSTNet 0.801 fe - - softmax+ce, mmd loss, got loss 1:1:0.1 l 100
ShanghaiHangzhou DNN 0.911±0.020 fe 540 average softmax+ce 1 - 64
ShanghaiHangzhou DDC 0.928±0.004 fe 540 average softmax+ce, mmd loss 1:1 g1 64
ShanghaiHangzhou DAN 0.913±0.011 fe 540 average softmax+ce, mmd loss 1:1 g5 64
ShanghaiHangzhou JAN 0.905±0.014 fe 540 average softmax+ce, joint mmd loss 1:1 g5 64
ShanghaiHangzhou DSAN 0.901±0.013 fe 540 average softmax+ce, local mmd loss 1:1 g5 64
ShanghaiHangzhou DANN 0.910±0.010 fe 540 average softmax+ce 1 - 64
ShanghaiHangzhou MCD 0.930±0.004 fe 540 average softmax+ce, l1 loss 1:1 - 64
ShanghaiHangzhou Self-training 0.925±0.000 fe 540 average softmax+ce, cbst loss 1:1 - 64
ShanghaiHangzhou DST 0.927±0.015 fe 540 average softmax+ce, wcec loss, cbst loss 1:1:1 - 64
ShanghaiHangzhou DADST 0.933±0.004 fe 540 average softmax+ce, wcec loss, adv loss, cbst loss 1:1:1:1 - 64

结果

This project is released under the MIT(LICENSE) license.