MLFC-net: A multi-level feature combination attention model for remote sensing scene classification

The image labeling task of remote sensing image scene classification (RSSC) is based on the semantic content of remote sensing images. The semantic information within remote sensing photographs has become more complicated and difficult to detect as remote sensing technology has progressed. As a result, extracting more important semantic elements could aid in the completion of the RSSC assignment. Thus, in this research, we offer MLFC-Net, a multi-level semantic feature clustering attention model based on deep convolution neural networks (DCNNs) that extracts more accurate feature information. The concept of MLFC-Net stems from the utilization of rich spatial information found in remote sensing photos, but few approaches in the RSSC application considered merging general semantic feature information with clustered semantic feature information. By rearranging the weight of corresponding information, such as feature maps and tensor blocks of the feature map, we implemented the attention mechanism. To build a model with minimal computational cost and good portability, we use a channel-wise attention mechanism and an ensemble structure. We were able to improve the representation of several critical semantic aspects using the MLFC model. In the EuroSAT, UCM, and NWPU-RESISC45 RSSC datasets, the MLFC model's performance is demonstrated. And, on average, the MLFC model enhanced accuracy by 2.56 percent, 1.25 percent and 2.00 percent, respectively, producing results that were equivalent to the stateof-the-art.

Automated summary

This research introduces MLFC-Net, a multi-level semantic feature clustering attention model based on deep convolution neural networks (DCNNs), which efficiently extracts accurate feature information for remote sensing image scene classification. The model improves the representation of critical semantic aspects and achieves state-of-the-art results on multiple RSSC datasets.