Remote sensing Image processing, DEEP learning Classification algorithms survey and review | ||||
| Journal of Advanced Engineering Trends | ||||
| Volume 43, Issue 1, January 2024, Page 239-267 PDF (2.57 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/jaet.2023.142870.1184 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Ibrahim Moahmed Amer  1; Kamel Rahouma 2; Attia Abd AlFattah Shahin3
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| 1Head of Optical payload test lab, Assembly integration and test Center, Egyptian Space Agency, Cairo, Egypt | ||||
| 2Minia University | ||||
| 3NARSS | ||||
| Abstract | ||||
| Image processing is considered as key in remote sensing fields. It is very wide and it has many branches. One of the major helpful widely used techniques in image processing, is image classification. Image classification is used to classify the extracted features from the digital images into different classes based on different characteristics. Machine learning and deep learning are two main techniques to automate the processes of image processing and classification. This paper introduces a survey on the recently used classification techniques using deep learning. This paper aims to provide a review about the newly presented theories and tools used in deep learning for remote sensing and satellite images since 2018 till 2020 Deep learning, as a subset of machine learning needs high processing devices like microprocessor, FPGA and GPUs. Recently, due to the progress in GPUs and other processors, it becomes much easier to deal with DL for BIG DATA and satellite images | ||||
| Keywords | ||||
| Deep learning; remote sensing; hyperspectral; image classification | ||||
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