Remote sensing image scene classification using CNN-MLP with data augmentation

Classification of the very high-resolution (VHR) imagery scene has become a challenging problem. The convolutional neural network (CNN) has increased the accuracy in this area due to learning features. However, models based on CNN contain many deep layers for classifying images that are not perfect in describing the relationship between objects within the image. Therefore, an enhanced multilayer perceptron (MLP) depending on Adagrad optimizer is employed in the classification step in this paper as a deep classifier. Motivated by this idea, this paper proposes an effective classification model named CNN-MLP to utilize the benefits of these two techniques: CNN and MLP. The features are generated using pre-trained CNN without fully connected layers. Due to limited training images per class, the proposed model uses data augmentation techniques to expand the training images. Then, an MLP is used to classify the final feature maps into the specified classes. Three public remote sensing datasets of VHR images to evaluate the proposed CNN-MLP model: UC-Merced, Aerial Image (AID), and NWPU-RESISC45 datasets. The experiment's findings show that the proposed method will contribute to higher classification performance relative to state-of-the-art methods.