The classification of the land-cover like forest, urban, water/ice, farm- land i.e. crop, oil-slick is important for controlling deterioration of the environment and destruction of wetland, for urban region planning, natural resources monitoring and for collecting information on possible future disasters. Land-cover classification can be implemented using images acquired from various types of sensors. Synthetic Aperture Radar (SAR) is a radar that collects information from the earth surface and generates high resolution images of wide areas. The convolutional neural network, is a specialized type of neural network model designed for working with two-dimensional image data, although they can be used with one-dimensional and three-dimensional data. The proposed method adopts the idea of deep neural networks and presents a hybrid ResNet and VGG with Support Vector Machine (SVM) based convolutional neural network to classify the land cover from high-resolution remote sensing imagery. Pretrained versions of ResNet, VGG, and AlexNet were used for the classification in order to compare and assess the suggested method's superiority. Greater accuracy of 96.3 %is offered by the hybrid ResNet-VGG withSVM model than by the pretrained ResNet and VGG. Compared to the existing models, the proposed model shows better performance in terms of accuracy, precision, recall and dice-index.