Remote Sensing Image Scene Classification using Dilated Convolutional Neural Networks

Abstract

Remote sensing image (RSI) scene classification has received growing attention from the research community in recent days.Over the past few decades, with the rapid development of deep learning models particularly convolutional neural networks (CNN), the performance of RSI scene classification have been drastically improved due to the hierarchical feature representation learning through CNN.But, we found that these models suffer for characterizing complex patterns in remote sensing imagery because of small inter class variations and large intra class variations.In order to solve these problems, we have proposed a Dilated Convolutional Neural Network (D-CNN) to improve the performance of RSI scene classification.The aim of dilated convolution filter is to incorporate more relevant information by increasing the receptive field of convolutional layer.In addition to traditional CNN model, it increases CNN efficiency and reduce computational time.For evaluating the proposed approach, we have collected three publicly available benchmark datasets namely, NWPU 45-class, PatternNet and Aerial Image Dataset (AID).Finally, experimental results are demonstrated for our proposed model using above dataset and achieved 89.85%, 92.35% and 97.18% respectively, which is outperformed traditional CNN model.