We use several semantic segmentation models to deal remote sensing images classifcation.

Semantic segmentation networks:U-net,PSPNet, FPN, LinkNet, DeepLab V3+

Backones: VGG:'vgg16' 'vgg19'

ResNet:'resnet18' 'resnet34' 'resnet50' 'resnet101' 'resnet152'

SE-ResNet:'seresnet18' 'seresnet34' 'seresnet50' 'seresnet101' 'seresnet152'

ResNeXt:'resnext50' 'resnext101'

SE-ResNeXt:'seresnext50' 'seresnext101'

SENet154:'senet154'

DenseNet:'densenet121' 'densenet169' 'densenet201'

Inception:'inceptionv3' 'inceptionresnetv2'

MobileNet:'mobilenet' 'mobilenetv2'

EfficientNet:'efficientnetb0' 'efficientnetb1' 'efficientnetb2' 'efficientnetb3'

Requirements:

tensorflow-gpu==1.9.0(only test on V1.9.0) keras>=2.2.4 keras_applications==1.0.7 image-classifiers==0.2.0 efficientnet>=0.0.3 cuda==9.0 qt==5.6 numpy==1.12.0 scipy==0.19.1 tqdm==4.11.2