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- W4378419780 abstract "Remote Sensing has been a hot topic in recent years. As satellite imagery has improved spatially and spectrally in recent years, its quality has improved as well. Remote Sensing (RS) has been able to provide a lot of information that is easily interpreted. High-tech remote sensing imagery still faces the problem of selecting and combining appropriate features according to their spectral and spatial properties. In this study, the pretrained AlexNet technique is proposed to classify the satellite images. The pretrained deep learning network Alexnet is implemented for enhanced image and the raw data. For the enhancement technique, an Adaptive Median Filter has been used. A deep learning technique based on Adaptive Median Filter can be used to classify satellite images accurately based on the results obtained. AlexNet gives 87.5% accuracy for 8 class classification for enhanced image and AUC value of the enhanced image is 0.8915." @default.
- W4378419780 created "2023-05-27" @default.
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- W4378419780 date "2023-01-01" @default.
- W4378419780 modified "2023-09-27" @default.
- W4378419780 title "Multilevel Classification of Satellite Images Using Pretrained AlexNet Architecture" @default.
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- W4378419780 doi "https://doi.org/10.1007/978-3-031-34222-6_17" @default.
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