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

• Published in: International journal of remote sensing Vol. 44; no. 21; pp. 6615 - 6641
• Main Authors: Shi, Cuiping, Zhang, Xinlei, Wang, Liguo, Jin, Zhan
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 02-11-2023; Taylor & Francis Ltd
• Subjects: Artificial neural networks; Classification; Context; Convolution; Feature extraction; global context feature; Image classification; joint feature; Labels; Lightweight; lightweight convolutional neural network (LCNN); Modules; Neural networks; Remote sensing; remote sensing scene image; Representations; Semantics
• ISSN: 0143-1161; 1366-5901
• DOI: 10.1080/01431161.2023.2273246

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.