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Remote sensing scene classification with multi-spatial scale frequency covariance pooling
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DOI：10.1007/s11042-022-12603-x.png)
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To address the problem of redundant learning in remote sensing scene classification, a method of multi-space-scale frequency covariance pooling (MSFCP) is proposed in this study. Specifically, a Gabor filter is introduced to the network which reduced redundant learning in ordinary convolution filters and enhanced the robustness of the network to external interference. Secondly, reducing redundant information in low-frequency components via dividing the feature map output by the first layer into high and low-frequencies and performing average pooling for low-frequency information. Next, the introduction of the Octave Convolution (OctConv) operation realized self-update and information interaction of high and low-frequency characteristics. Finally, the global covariance pooling is performed on the output feature map to enhance the representation ability of the entire network and boost the classification effect. Our method performed an accuracy value of 99.35 +/- 0.28 (%) on the UC Merced Land Use dataset. The experimental results demonstrate that the proposed MSFCP method achieves better classification performance and lower network model complexity than other methods, which significantly reduces the demand for computing power. Hence, a good trade-off is achieved between experimental accuracy and computational resource consumption.
Keyword:
Redundant learning
Scene classification
Gabor filter
Covariance pooling
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
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