Terrain Classification Algorithm for Lunar Rover Using a Deep Ensemble Network with High-Resolution Features and Interdependencies between Channels. (14th October 2020)

• Record Type: Journal Article
• Authors: Zhou, Lanfeng; Liu, Ziwei; Wang, Wenfeng
• Other Names: Zhang Yin Academic Editor.
• Is Part Of: Wireless communications and mobile computing. Volume 2020(2020)
• Publication Date: 2020-10-14
• Subjects: Wireless communication systems -- Periodicals; Mobile communication systems -- Periodicals; 621.38205
• DOI: 10.1155/2020/8842227
• Languages: English
• ISSNs: 1530-8669

Abstract

Abstract : For terrain classification tasks, previous methods used a single scale or single model to extract the features of the image, used high-to-low resolution networks to extract the features of the image, and used a network with no relationship between channels. These methods would lead to the inadequacy of the extracted features. Therefore, classification accuracy would reduce. The samples in terrain classification tasks are different from in other image classification tasks. The differences between samples in terrain classification tasks are subtler than other image-level classification tasks. And the colours of each sample in the terrain classification are similar. So we need to maintain the high resolution of features and establish the interdependencies between the channels to highlight the image features. This kind of networks can improve classification accuracy. To overcome these challenges, this paper presents a terrain classification algorithm for Lunar Rover by using a deep ensemble network. We optimize the activation function and the structure of the convolutional neural network to make it better to extract fine features of the images and infer the terrain category of the image. In particular, several contributions are made in this paper: establishing interdependencies between channels to highlight features and maintaining a high-resolution representation throughout the process to ensure the extraction of fine features. Multimodel collaborative judgment can help make up for the shortcomings in the design of the single model structure, make the model form a competitive relationship, and improve the accuracy. The overall classification accuracy of this method reaches 91.57% on our dataset, and the accuracy is higher on some terrains.