Remote Sensing Image Scene Classification Based on Multi-level Feature Fusion

Abstract

Remote sensing image scene classification is an important task in the fields of remote sensing image analysis and interpretation. Convolutional neural networks (CNNs) has been a representative image classification network owing to its capacity of feature learning and representation. Although the semantic information of feature map was enhanced along with the convolution layer increases, the spatial geometric detail information may be lost, which will greatly affect the accuracy of classification. To solve these problem, we proposed a multi-level convolutional feature fusion network (MLCF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> N) for remote sensing image scene classification. Specifically, the original image is fed to the multi-level feature extraction network to extract features of different levels by increasing the depth of the convolution layer. And then, in order to highlight the key interest areas of all levels, we design a key information weight network to obtain feature map of the discriminative area in each level feature. Finally, we fuse the low-level features, middle- level features and high- level features to get more discriminative features for scene classification. Experiments are conducted on several public remote sensing image scene classification datasets: AID, NWPU-RESISC45 and OPTIMAL 31 to evaluate the performance of the method in this paper. The experimental results show that multi- level feature fusion classification is better than single- level feature classification and some of the state of art methods.