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.
Highlights
Terrain classification is important in the driving process of a lunar rover, especially in complex terrain environments
Combined with the above analysis, a new terrain classification method based on the combination of convolutional neural network and ensemble learning is proposed
In Materials and Methods, we review the previous methods, standards for terrain classification, and the theoretical basis of the convolutional neural network (CNN)
Summary
Terrain classification is important in the driving process of a lunar rover, especially in complex terrain environments. Lauro Ojeda of the University of Michigan did some new research on terrain classification and some terrain descriptions [9] They used a fully connected neural network with only one hidden layer to classify the terrain. Zeltner [13] used a deep convolutional neural network to implement a vision-based terrain classification. Combined with the above analysis, a new terrain classification method based on the combination of convolutional neural network and ensemble learning is proposed. In Materials and Methods, we review the previous methods, standards for terrain classification, and the theoretical basis of the convolutional neural network (CNN). We take these as the basis of our research.
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