In particular, we review architectures within the deep learning literature and their use cases.
📌 3 Key Takeaways
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Objective & Hypothesis:In recent years, deep learning methods have come to the forefront in many areas that require remote sensing, from medicine to agriculture, from defense industry to space research; and these methods have given more successful results as compared to traditional methods.
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Key Findings & Data:The major difference between deep learning and classical recognition methods is that deep learning methods consider an end-to-end learning scheme which gives rise to learning features from raw data.
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Conclusion & Impact:In particular, we review architectures within the deep learning literature and their use cases.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Applications in Defense and Security · Applications in Medicine · Deep Learning
Abstract
In recent years, deep learning methods have come to the forefront in many areas that require remote sensing, from medicine to agriculture, from defense industry to space research; and these methods have given more successful results as compared to traditional methods. The major difference between deep learning and classical recognition methods is that deep learning methods consider an end-to-end learning scheme which gives rise to learning features from raw data. In this study, we discuss the remote sensing problems and how deep learning can be used to solve these problems with a special focus on medical and defense applications. In particular, we review architectures within the deep learning literature and their use cases.
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