Convolutional Neural Network With Data Augmentation for SAR Target Recognition

By Jun Ding, Bo Chen, Hongwei Liu, Mengyuan Huang. IEEE Geoscience and Remote Sensing Letters, 2016

681 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computational Mechanics

View Paper: https://www.semanticscholar.org/paper/1ed0dcd304219d8469ae64a6cf17bb75abb92776

How researchers see this paper

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

In Aerospace Engineering

1. Richard Bamler
2. Xiaoxiang Zhu
3. Yang Long
4. Bin Lei
5. Feng Xu
6. Zongxu Pan
7. Lichao Mou
8. Mohammad Rostami

In Computational Mechanics

1. Richard Bamler
2. Xiaoxiang Zhu
3. Lichao Mou

Citations in the corpus

• A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, background): “Data augmentation (Ding et al., 2016; Morgan, 2015) means increasing the training data by”
• Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data (2017, methodology): “They found that support vector machine (SVM) was the most efficient for most applications with an overall accuracy (OA) of about 75%.”
• Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives (2020, methodology): “(patches) of 10 classes of different vehicles plus one class of simple geometric shaped target. SAR images of pure clutter are also included in the dataset. object detection, scene classification [88] [90] [193] OpenSARShip 2.09 [194] 34,528 Sentinel-1 SAR image chips of ships with the ship geometric”
• Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease (2019, background): “There are various adjustable parameters like the dropout rate, the learning rate used in complex processing for solving classification and pattern recognition problems [24], [25].”
• Accurate Object Localization in Remote Sensing Images Based on Convolutional Neural Networks (2017, methodology): “[44] investigated the capabilities of a CNN model combined with data augmentation operations in SAR target recognition.”
• Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data (2017, background): “Limited by inadequate data in SAR target recognition, the current studies related to deep CNNs mainly focus on augmenting the training data [23], designing a less complex network for a specific problem and making efforts on avoiding overfitting [24].”
• SAR Automatic Target Recognition Based on Multiview Deep Learning Framework (2018, methodology): “Although there are means to extend the training inputs [23], they do not increase the recognition information of the raw SAR data.”