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- W2906825611 abstract "Image classification is very crucial for the information extraction and with the availability of high resolution and abundant data, traditional classifiers are not enough for such datasets. The solution is provided by Deep learning theory which allows for automated feature extraction method with an improved accuracy. Deep learning is a subset of machine learning and it has blossomed over the last decade because of its feature learning capability and its ability to generalize over unseen dataset. These techniques have been proven suitable in many fields such as biomedical, audio and video recognition, hand writing recognition and now it is also being used for the classification of aerial and satellite imagery. A simple Convolutional Neural Network is used for DeepSat dataset which gave overall accuracy of 99.97% for SAT-4, 99.98% for SAT-6. The results are at par and even surpassed some of the previous studies. Performance evaluation shows that the learning rate of 0.1 and batch size 250 provides with the best results for both SAT-6 and SAT-4. For a developing country like Pakistan, there is a dire need for a dataset similar to DeepSat which can then be used for various applications." @default.
- W2906825611 created "2019-01-11" @default.
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- W2906825611 date "2018-10-01" @default.
- W2906825611 modified "2023-09-28" @default.
- W2906825611 title "Benchmarking Deepsat Dataset with a Simple Convolutional Neural Network" @default.
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- W2906825611 doi "https://doi.org/10.1109/iaeac.2018.8577841" @default.
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