A Lightweight Model of VGG-16 for Remote Sensing Image Classification
In planetary science, it is an important basic work to recognize and classify the features of topography and geomorphology from the massive data of planetary remote sensing. Therefore, this article proposes a lightweight model based on VGG-16, which can selectively extract some features of remote se...
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| Veröffentlicht in: | IEEE journal of selected topics in applied earth observations and remote sensing 2021, Vol.14, p.6916-6922 |
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| container_title | IEEE journal of selected topics in applied earth observations and remote sensing |
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| creator | Ye, Mu Ruiwen, Ni Chang, Zhang He, Gong Tianli, Hu Shijun, Li Yu, Sun Tong, Zhang Ying, Guo |
| description | In planetary science, it is an important basic work to recognize and classify the features of topography and geomorphology from the massive data of planetary remote sensing. Therefore, this article proposes a lightweight model based on VGG-16, which can selectively extract some features of remote sensing images, remove redundant information, and recognize and classify remote sensing images. This model not only ensures the accuracy, but also reduces the parameters of the model. According to our experimental results, our model has a great improvement in remote sensing image classification, from the original accuracy of 85%-98% now. At the same time, the model has a great improvement in convergence speed and classification performance. By inputting the remote sensing image data of ultra-low pixels (64 * 64) into our model, we prove that our model still has a high accuracy rate of 95% for the remote sensing image with ultra-low pixels and less feature points. Therefore, the model has a good application prospect in remote sensing image fine classification, very low pixel, and less image classification. |
| doi_str_mv | 10.1109/JSTARS.2021.3090085 |
| format | Article |
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Therefore, this article proposes a lightweight model based on VGG-16, which can selectively extract some features of remote sensing images, remove redundant information, and recognize and classify remote sensing images. This model not only ensures the accuracy, but also reduces the parameters of the model. According to our experimental results, our model has a great improvement in remote sensing image classification, from the original accuracy of 85%-98% now. At the same time, the model has a great improvement in convergence speed and classification performance. By inputting the remote sensing image data of ultra-low pixels (64 * 64) into our model, we prove that our model still has a high accuracy rate of 95% for the remote sensing image with ultra-low pixels and less feature points. 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Therefore, this article proposes a lightweight model based on VGG-16, which can selectively extract some features of remote sensing images, remove redundant information, and recognize and classify remote sensing images. This model not only ensures the accuracy, but also reduces the parameters of the model. According to our experimental results, our model has a great improvement in remote sensing image classification, from the original accuracy of 85%-98% now. At the same time, the model has a great improvement in convergence speed and classification performance. By inputting the remote sensing image data of ultra-low pixels (64 * 64) into our model, we prove that our model still has a high accuracy rate of 95% for the remote sensing image with ultra-low pixels and less feature points. 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| subjects | Accuracy Adaptation models Classification Convolution Data models Feature extraction Geomorphology Hyperspectral imaging Image classification less feature points Lightweight nonlinear correction layer Pixels Remote sensing Sensors Training Vgg-16 zero padding |
| title | A Lightweight Model of VGG-16 for Remote Sensing Image Classification |
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