A Dual-Model Architecture with Grouping-Attention-Fusion for Remote Sensing Scene Classification

Abstract

Remote sensing images contain complex backgrounds and multi-scale objects, which pose a challenging task for scene classification. The performance is highly dependent on the capacity of the scene representation as well as the discriminability of the classifier. Although multiple models possess better properties than a single model on these aspects, the fusion strategy for these models is a key component to maximize the final accuracy. In this paper, we construct a novel dual-model architecture with a grouping-attention-fusion strategy to improve the performance of scene classification. Specifically, the model employs two different convolutional neural networks (CNNs) for feature extraction, where the grouping-attention-fusion strategy is used to fuse the features of the CNNs in a fine and multi-scale manner. In this way, the resultant feature representation of the scene is enhanced. Moreover, to address the issue of similar appearances between different scenes, we develop a loss function which encourages small intra-class diversities and large inter-class distances. Extensive experiments are conducted on four scene classification datasets include the UCM land-use dataset, the WHU-RS19 dataset, the AID dataset, and the OPTIMAL-31 dataset. The experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-arts.

A Lightweight Convolutional Neural Network Based on Hierarchical-Wise Convolution Fusion for Remote-Sensing Scene Image Classification

“…The information exchange of convolution-fused features was carried out through the channel shuffling operation to improve the performance of the network. Shen et al [27] adopted group attention fusion strategy to improve network classification performance.…”

Remote sensing scene image classification model based on multi-scale features and attention mechanism

“…The spatial domain attention module considers all information of channels and learns the coefficients for each region to find the salient features. Shen et al 52 divide the feature map into three levels along the channels: low, medium, and high, and use the attention mechanism to enhance the features of each level separately to obtain salient regions with different perceptual domains. Li et al 53 applied the attention mechanism to a feature fusion framework to generate an attention map that uses a gradient-weighted activation map-like algorithm to focus on the critical regions of the image.…”

“…The spatial domain attention module considers all information of channels and learns the coefficients for each region to find the salient features. Shen et al 52 . divide the feature map into three levels along the channels: low, medium, and high, and use the attention mechanism to enhance the features of each level separately to obtain salient regions with different perceptual domains.…”

Decision-Level Fusion with a Pluginable Importance Factor Generator for Remote Sensing Image Scene Classification

“…In this section, we briefly review the related work of remote sensing scene classification. The existing methods with CNN could be divided into three categories: the conventional CNN features-based methods [12,24], the feature-level fusion-based methods [16][17][18][19][20][25][26][27][28][29][30][31][32][33][34] , and the decision-level fusion-based methods [35,36].…”