Aircraft classification in remote-sensing images

that is

identifying the types of aircraft in remote-sensing images rapidly and accurately

is undoubtedly helpful for providing military information and taking military advantages. This method has extreme potential in research on the field of processing optical remote-sensing images. Various conventional machine-learning methods exist to solve this problem. However

these methods are difficult to apply to real optical remote-sensing images because of the complicated background. Features also need to be selected when using these methods

and the classification performance greatly depends on what features are extracted. Artificial selection of features

which is time consuming and complicated

is usually required to gain a relatively good result. Deep convolutional neural network has gained popularity in recent years. This network

which can learn features by itself and shows excellent generalization capability

has been widely used in the areas of computer vision and pattern recognition. However

at present

convolutional neural networks are rarely applied to this issue. This study aims to solve the aircraft classification problem in optical remote-sensing images using convolutional neural networks. Considering the lack of public dataset of aircraft in optical remote-sensing images

this study collects eight types of aircraft from optical remote-sensing images to form a dataset

including boomers

carriers

fighters

primary trainers

and tankers. The number of each aircraft is equal. The dataset is divided into training and testing sets

with the ratio between the sizes of these two at 4:1. The samples are randomly selected into training or testing dataset. For each aircraft type

the ratio between the number of training and testing datasets remains the same. The training set is largely augmented with three methods when training because of the large need for data of the convolutional neural network. The final training set is as large as 32 times that of the original training set. This study designs a special five-layer convolutional neural network for aircraft classification in optical remote-sensing images based on the theory of deep convolutional neural network. The sizes of convolutional and pooling kernels used in shallow layers are as small as 3×3 pixels to adapt to the specialty of aircraft classification in remote-sensing images

such as the lack of data and the low absolute resolution. Initially

this convolutional neural network is trained individually on different training sets

which are augmented in varying degrees. In addition

well-trained convolutional neural networks are tested using the same test set. The result shows that the test accuracy can be improved from 72.4% to 97.2% by augmenting the training set. Second

this paper trains and tests the aforementioned five-layer convolutional neural network on the aircraft dataset. This convolutional neural network is also individually trained on original and augmented training dataset to guarantee that data augmentation is necessary. Moreover

the gained networks work on the same test dataset. According to the aforementioned experiment result

the network trained on the largest training dataset performs best. Therefore

data augmentation is suitable and necessary

and the dataset mentioned in the following experiment is augmented. After establishing the dataset

this paper selected one type of classical conventional machine learning method for comparison to thoroughly verify the feasibility of the convolutional neural network. Furthermore

LeNet-5

the first utilitarian convolutional neural network

is used to identify the eight types of aircraft for comparison. The three methods used the same dataset in the training and testing phases. The experimental result shows that the classification accuracy obtained as high as 97.2% with the five-layer convolutional neural network design. By contrast

the accuracies with the other two algorithms are 82.3% and 88.7% respectively. Therefore

the designed five-layer convolutional neural network performed better than the classical conventional machine learning method and LeNet-5. This result is attributed to the fact that the conventional machine learning method requires image segmentation

which is difficult to perform in complicated scenarios. Moreover

LeNet-5 is primarily used for digital number recognition rather than aircraft classification. Thus

LeNet-5 is relatively smaller and not quite suitable for this issue. This paper designs a five-layer convolutional neural network specifically for aircraft classification in optical remote-sensing images

in which deep convolutional neural network is incapable to see. The experiments show that this convolutional neural network can effectively acquire the aircraft features and classify with high accuracy.