Review of Image Classification Algorithms Based on Convolutional Neural Networks

By Leiyu Chen, Shaobo Li, Qian Bai, Jing Yang, Sanlong Jiang, Yanming Miao. Remote Sensing, 2021

697 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/774e0cce8bdd7f1ef41841dcdef058ac630f5db3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ji Qi
2. Thinagaran Perumal
3. Norwati Mustapha
4. Haifeng Li
5. Qing Zhu
6. Mingning Guo
7. Raihani Mohamed
8. Chao Tao

In Computer Vision and Pattern Recognition

1. Ji Qi
2. Thinagaran Perumal
3. Norwati Mustapha
4. Haifeng Li
5. Qing Zhu
6. Mingning Guo
7. Raihani Mohamed
8. Chao Tao

In Media Technology

1. Ji Qi
2. Haifeng Li
3. Qing Zhu
4. Mingning Guo
5. Chao Tao

Citations in the corpus

• A Comprehensive Overview and Comparative Analysis on Deep Learning Models: CNN, RNN, LSTM, GRU (2023, background): “In image classification, lower layers tend to capture basic features such as texture, lines, and edges, while higher layers extract more abstract features [16].”
• Self-Supervised Remote Sensing Feature Learning: Learning Paradigms, Challenges, and Future Works (2022, background): “Instead of traditional hand-crafted feature engineering, learning feature representation from massive data has attracted great attention during the past decade, and exhibited very impressive performance in many RSI understanding tasks [7]– [12].”