Application of deep learning based super resolution methods to satellite images and improvement of images

High resolution of the image used for all important applications in military and civil life is very important. In works with satellite images, the use of images enhanced with super resolution is necessary in applications such as building detection. In the super resolution algorithms where the low-resolution image is given as input, high resolution image is obtained as a result of various improvement steps. The performance of super resolution improvement with deep learning based convolutional neural networks on 900 images taken from available satellite images was analyzed. Test and training data are reserved for deep learning on the dataset. A total of 6 softmax functions (AlexNet, DenseNet201, ResNet50, SqueezeNet, Vgg16, Vgg19) were applied to the data separately. Evolutionary neural networks were applied to control the number of correctly classified data before and after the super resolution step. The classification results are compared and as a result of the classification, the learning properties of the convolutional neural networks are increased by super resolution. Classification success has proven to be increased by the lowest 2.4% and the highest 3.6% for the 6 classification architectures.