Paper Info
Title
MLFC-net: A multi-level feature combination attention model for remote sensing scene classification
Abstract
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.
Year
DOI
Venue
2022
10.1016/j.cageo.2022.105042
COMPUTERS & GEOSCIENCES
Keywords
DocType
Volume
Deep neural network, Remote sensing scene classification, CNN, Attention
Journal
160
ISSN
Citations 
PageRank 
0098-3004
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Deyi Wang100.68
Chengkun Zhang232.40
Min Han376168.01