Abstract
In recent years, with the rapid development of computer vision, increasing attention has been paid to remote sensing image scene classification. To improve the classification performance, many studies have increased the depth of convolutional neural networks (CNNs) and expanded the width of the network to extract more deep features, thereby increasing the complexity of the model. To solve this problem, in this paper, we propose a lightweight convolutional neural network based on attention-oriented multi-branch feature fusion (AMB-CNN) for remote sensing image scene classification. Firstly, we propose two convolution combination modules for feature extraction, through which the deep features of images can be fully extracted with multi convolution cooperation. Then, the weights of the feature are calculated, and the extracted deep features are sent to the attention mechanism for further feature extraction. Next, all of the extracted features are fused by multiple branches. Finally, depth separable convolution and asymmetric convolution are implemented to greatly reduce the number of parameters. The experimental results show that, compared with some state-of-the-art methods, the proposed method still has a great advantage in classification accuracy with very few parameters.
Highlights
Remote sensing image scene classification refers to the use of aerial scanning, microwave radar, and other methods to image the target scene and extract useful information from different scene images, enabling an analysis and evaluation of the scene image
Compared with the two-stage deep feature fusion [17] method, the SPM-CRC [40] method, the WSPMCRC [40] method, and the LCNN-BFF [49] method, the overall accuracy (OA) of the proposed method is improved by 2.77%, 1.28%, 1.24%, and 0.50%, respectively
The proposed AMB-Convolution neural networks (CNNs) method is evaluated on four datasets with different division proportions, and is proven to have a good classification performance
Summary
Remote sensing image scene classification refers to the use of aerial scanning, microwave radar, and other methods to image the target scene and extract useful information from different scene images, enabling an analysis and evaluation of the scene image. Relevant research on remote sensing scene classification has been widely used in national defense security [1], analyses of crop growth [2], and environmental management [3]. In recent years, some researchers have focused on the effective scene classification of remote sensing images. With the development of imaging technology and hardware equipment, deep learning has become widely used in remote sensing image scene classification and has natural advantages. Convolution neural networks (CNNs) can extract rich feature details from images and are used by most researchers [4,5,6]. Researchers are expanding the depths and widths of the neural networks to improve the performance of image classification. CNN has a certain role, the demand for computing equipment continues to increase, as does the necessary calculation time of the model
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