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Review of Image Classification Algorithms Based on Convolutional Neural Networks

Remote sensing (Basel, Switzerland), 2021-11, Vol.13 (22), p.4712 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs13224712

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  • Title:
    Review of Image Classification Algorithms Based on Convolutional Neural Networks
  • Author: Chen, Leiyu ; Li, Shaobo ; Bai, Qiang ; Yang, Jing ; Jiang, Sanlong ; Miao, Yanming
  • Subjects: Algorithms ; Artificial neural networks ; Back propagation ; Classification ; Computer architecture ; convolutional neural networks ; Deep learning ; Image classification ; Image segmentation ; Localization ; Machine learning ; Neural networks ; Neurons ; Object recognition ; Remote sensing ; Researchers ; Visual tasks
  • Is Part Of: Remote sensing (Basel, Switzerland), 2021-11, Vol.13 (22), p.4712
  • Description: Image classification has always been a hot research direction in the world, and the emergence of deep learning has promoted the development of this field. Convolutional neural networks (CNNs) have gradually become the mainstream algorithm for image classification since 2012, and the CNN architecture applied to other visual recognition tasks (such as object detection, object localization, and semantic segmentation) is generally derived from the network architecture in image classification. In the wake of these successes, CNN-based methods have emerged in remote sensing image scene classification and achieved advanced classification accuracy. In this review, which focuses on the application of CNNs to image classification tasks, we cover their development, from their predecessors up to recent state-of-the-art (SOAT) network architectures. Along the way, we analyze (1) the basic structure of artificial neural networks (ANNs) and the basic network layers of CNNs, (2) the classic predecessor network models, (3) the recent SOAT network algorithms, (4) comprehensive comparison of various image classification methods mentioned in this article. Finally, we have also summarized the main analysis and discussion in this article, as well as introduce some of the current trends.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2072-4292
    EISSN: 2072-4292
    DOI: 10.3390/rs13224712
  • Source: AUTh Library subscriptions: ProQuest Central
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