A lightweight convolution neural network based on joint features for Remote Sensing scene image classification

ABSTRACT

Unlike natural images, remote sensing scene images usually contain one scene label and many object labels, and many object labels are arranged dispersedly, which brings great difficulties to feature extraction of scene label. To accurately identify scene labels from remote sensing scene images with multiple object labels, it is important to fully understand the global context of the image. In order to solve the challenges of multi-label scene images and improve the classification performance, a global context feature extraction module is proposed in this paper. The module combines the semantics information of different regions through a global pooling and three different scale sub-regions pooling, which makes the module have stronger ability of global feature representation. In addition, in order to fully understand the semantic content of remote sensing images, a three branch joint feature extraction module is constructed, which consists of the global context feature module, 3 × 3 convolution branch and identity branch are fused. Finally, a lightweight convolution neural network based on joint features (LCNN-JF) is constructed using traditional convolution, depthwise separable convolution, joint feature extraction module and classifier for remote sensing scene image classification. A series of experimental results on four datasets, UCM, AID, RSSCN and NWPU, demonstrate that the proposed method has better feature representation ability and can achieve better classification of remote sensing scene images.

KEYWORDS:

• global context feature
• joint feature
• lightweight convolutional neural network (LCNN)
• remote sensing scene image

Acknowledgements

The authors would like to thank the editors and the reviewers for their help and suggestion.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data associated with this research are available online. The UCM Merced dataset is available for download at http://weegee.vision.ucmerced.edu/datasets/landuse.html. NWPU dataset is available for download at http://www.escience.cn/people/JunweiHan/NWPURESISC45.html. AID dataset is available for download at https://captain-whu.github.io/AID/. SIRI-WHU dataset is available for download at http://www.lmars.whu.edu.cn/prof_web/zhongyanfei/ecode.html. WHU-RS19 dataset is available for download at https://paperswithcode.com/dataset/whu-rs19.

Additional information

Funding

This research was funded in part by the National Natural Science Foundation of China (42271409), in part by the Heilongjiang Science Foundation Project of China under Grant LH2021D022, in part by the Leading Talents Project of the State Ethnic Affairs Commission, and in part by the Fundamental Research Funds in Heilongjiang Provincial Universities of China under Grant 145209149.