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Landcover Classification on Salinas (test)

100Overall Accuracy (OA)HybridSN
85.741689.443393.14596.8467Dec 1, 2016Nov 1, 2017Oct 3, 2018Sep 3, 2019Aug 4, 2020Jul 5, 2021Jun 6, 2022

Evaluation results

MethodLinks
HybridSN2022.06100————————————————100100
JigsawHSI2022.06100————————————————100100
FreeNetInput Processing=Patch-free2020.1199.9210010010099.9299.1110010099.9410099.7710010010010099.9999.8899.910.9991
DFFNInput Processing=Patch-based2020.1199.7199.9999.9410010099.1199.9599.4399.5610099.7999.4899.8499.9699.9599.4599.9699.780.9967
3D-GANInput Processing=Patch-based2020.1198.2275.3598.4910099.7610099.9799.4598.399.9599.7910010010010099.5290.2597.750.9793
Gabor-CNNInput Processing=Patch-based2020.1197.6310088.0699.2510098.8810098.9499.4897.2799.2110010010099.7994.3193.3898.040.9734
S-CNNInput Processing=Patch-based2020.1197.6210097.7598.8810099.9389.4899.398.6999.3410099.9399.8988.6288.7290.6299.9696.940.951
BASS Net2016.1295.3610099.9710099.6699.5910099.9190.1199.7397.4699.0810099.4410083.9499.38——
PPFImplementation=Torch, Reference=Li et al. [31]2016.1294.810099.8899.699.4998.3499.9710088.6898.3398.699.5410099.4498.9683.5399.31——
SVMImplementation=libsvm, Feature Selection=Random Feature Selection2016.1293.1599.5599.9299.4499.8698.0299.799.6984.8599.5896.4998.7810099.1398.9776.3899.56——
ELM2016.1292.4299.7599.8799.699.6498.8199.6799.6684.0499.8995.0396.8210098.2597.9472.9699.06——
SVMInput Processing=Patch-based2020.1190.9299.6199.6999.5699.4198.7299.7799.5276.7499.3795.1399.3299.799.1598.3875.5699.2396.180.8985
MLPArchitecture=Nc-150-100-50-C2016.1290.7899.6799.7798.3799.7598.8399.6899.2975.9699.2796.0797.9310099.5898.9675.9398.51——
CNNBackbone=Hu et al. [27]2016.1289.2897.3499.2996.5199.6696.9799.699.4972.2597.5391.2997.5810099.0295.0576.8398.94——
k-NNImplementation=scikit-learn2016.1286.2998.7199.6599.0999.7895.2899.4999.5563.5395.9491.9898.4199.8498.6997.3865.6699——