Landscape Classification Using an Optimized Ghost Network from Aerial Images

Abstract

Despite recent advances of Deep learning in numerous computer-vision tasks, the possibility of classifying aerial images has not been thoroughly explored. The aerial image classification purely depends on spectral content is an interesting research subject. In this work, a novel Optimized Ghost Network-based Aerial Image Classification (OGN-AIC) approach is proposed to classify the different Aerial images from the dataset. The image is first preprocessed using Gaussian filtering techniques to enhance its quality and remove noise. Consequently, the features are extracted using Ghost Network for classifying the different landscapes. The input images are classified into five different categories namely: Dryland, Forest, Airport, Mountain, and Parking. The classification results are improved by the Slime Mould optimization (SMO) algorithm, which normalizes the parameters of the network. The efficiency of the proposed OGN-AIC model was assessed utilizing precision, F1 score, specificity, sensitivity and accuracy. According to the experimental results, the proposed OGN-AIC model attains an overall accuracy of 98.24%. The proposed OGN-AIC technique enhances the overall accuracy range by 14.2%, 0.77%, 14.5%, 1.08%, and 11.17% better than Artificial Neural Networks, k-nearest neighbor, cutting-edge Deep Convolutional Neural Network (DCNN), semi-supervised Convolutional Neural Network and Cellular neural network respectively. As a result, the classification using a deep learning network is more accurate and effective for classifying aerial landscape images than the traditional DL techniques.