Review of Image Classification Algorithms Based on Convolutional Neural Networks
Top Cited Papers
Open Access
- 21 November 2021
- journal article
- review article
- Published by MDPI AG in Remote Sensing
- Vol. 13 (22), 4712
- https://doi.org/10.3390/rs13224712
Abstract
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.Keywords
This publication has 139 references indexed in Scilit:
- Receptive field properties of neurons in the primary visual cortex under photopic and scotopic lighting conditionsVision Research, 2007
- Local Invariant Feature Detectors: A SurveyFoundations and Trends® in Computer Graphics and Vision, 2007
- A Fast Learning Algorithm for Deep Belief NetsNeural Computation, 2006
- Review: Feature Extraction and Image ProcessingThe Computer Journal, 2004
- The Problem of OverfittingJournal of Chemical Information and Computer Sciences, 2003
- Gradient-based learning applied to document recognitionProceedings of the IEEE, 1998
- Sparse coding with an overcomplete basis set: A strategy employed by V1?Vision Research, 1997
- Support-vector networksMachine Learning, 1995
- Probability of error of some adaptive pattern-recognition machinesIEEE Transactions on Information Theory, 1965
- The perceptron: A probabilistic model for information storage and organization in the brain.Psychological Review, 1958