Review Article
Remote Sensing Image Classification: A Comprehensive Review and Applications
Table 3
Classification accuracy of AID on different CNN models.
| | Algorithm | Year | Publication | Accuracy (%) |
| | CNNS-WD [72] | 2019 | IEEE trans | 97.24 | | HW-CNN [87] | 2018 | IEEE trans | 96.98 | | DCNNS [87] | 2018 | IEEE trans | 96.89 | | RSFJR [71] | 2019 | IEEE trans | 96.81 | | SF-CNN [70] | 2019 | IEEE trans | 96.66 | | CNN-CAPSNET [88] | 2019 | Remote sensing | 96.32 | | SCCOV [89] | 2019 | IEEE trans | 96.10 | | GBN [70] | 2019 | IEEE trans | 95.48 | | FACNN [70] | 2019 | IEEE trans | 95.45 | | ADFF [74] | 2019 | IEEE trans | 94.75 | | MSCP [87] | 2018 | IEEE trans | 94.42 | | ARCNet-VGG16 [19] | 2017 | IEEE trans | 93.10 | | Inception-V3 [76] | 2017 | IEEE trans | 93 | | MCNN [16] | 2018 | IEEE trans | 91.80 | | VGG-VD-16 [78] | 2021 | Journal of Geosciences | 89.64 | | CaffeNet [81] | 2019 | Journal of Geovisualization and Spatial Analysis | 89.10 | | GoogLeNet [76] | 2017 | IEEE trans | 86.39 |
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