A Deep Learning SAR Target Classification Experiment on MSTAR Dataset

Coman, C.I.; Thaens, R.

doi:10.23919/irs.2018.8448048

Cited by 42 publications

(19 citation statements)

References 6 publications

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“…The project published the MSTAR dataset for research. At present, there are hundreds of papers based on this dataset, such as references [37][38][39][40].…”

Section: A Mstar Datasetmentioning

confidence: 99%

Synthetic Aperture Radar SAR Image Target Recognition Algorithm Based on Attention Mechanism

et al. 2021

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SAR images contain a large amount of noise, and related algorithms will cause high complexity when increasing the accuracy. To overcome this problem, a neural network model based on the attention mechanism was proposed in this paper. The model extracted information in two stages. It gradually extracts high-level features by reducing noise first and then adding hybrid attention. First, use dual-channel one-dimensional convolution to reconstruct the residual shrinkage network to construct a lightweight and efficient feature module, which improved the information extraction of the module with the consumption of a small amount of computing resources. Then, it was used as the backbone for model construction. Subsequently, mixed adaptive pooling was adopted to improve the maximum pooling. After that, dimensionality was reduced by pooling and linear interpolation was used to increase dimensionality, so as to generate feature weights of mixed dimension. Tests were performed on MSTAR dataset. The results showed that compared with the advanced algorithms, the proposed model in this paper can greatly reduce the amount of parameters and complexity while ensuring accuracy. The robustness test demonstrated that the model can effectively identify images with noise being added.

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“…The project published the MSTAR dataset for research. At present, there are hundreds of papers based on this dataset, such as references [37][38][39][40].…”

Section: A Mstar Datasetmentioning

confidence: 99%

Synthetic Aperture Radar SAR Image Target Recognition Algorithm Based on Attention Mechanism

et al. 2021

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“…Moving and Stationary Target Acquisition and Recognition (MSTAR) is a dataset [59] that contains baseline X-band SAR imagery of 13 target types plus minor examples of articulation, obscuration, and camouflage. The Sandia National Laboratory collected the dataset and Defense Advanced Research [60].…”

Section: Remote Sensing Classificationmentioning

confidence: 99%

Convolutional Extreme Learning Machines: A Systematic Review

Rodrigues¹,

Rogério²,

Kelner³

et al. 2021

Preprint

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Many works have recently identified the need to combine deep learning with extreme learning to strike a performance balance with accuracy especially in the domain of multimedia applications. Considering this new paradigm, namely convolutional extreme learning machine (CELM), we present a systematic review that investigates alternative deep learning architectures that use extreme learning machine (ELM) for a faster training to solve problems based on image analysis. We detail each of the architectures found in the literature, application scenarios, benchmark datasets, main results, advantages, and present the open challenges for CELM. We follow a well structured methodology and establish relevant research questions that guide our findings. We hope that the observation and classification of such works can leverage the CELM research area providing a good starting point to cope with some of the current problems in the image-based computer vision analysis.

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“…CNN is used for the classification of the Synthetic Aperture Radar images on datasets like MSTAR in [3]. The classification is done with and without additional radar information.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep learning-based explainable target classification for synthetic aperture radar images

Mandeep

Pannu

Malhi

2020

2020 13th International Conference on Human System Interaction (HSI)

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Deep learning has been extensively useful for its ability to mimic the human brain to make decisions. It is able to extract features automatically and train the model for classification and regression problems involved with complex images databases. This paper presents the image classification using Convolutional Neural Network (CNN) for target recognition using Synthetic-aperture Radar (SAR) database along with Explainable Artificial Intelligence (XAI) to justify the obtained results. In this work, we experimented with various CNN architectures on the MSTAR dataset, which is a special type of SAR images. Accuracy of target classification is almost 98.78% for the underlying preprocessed MSTAR database with given parameter options in CNN. XAI has been incorporated to explain the justification of test images by marking the decision boundary to reason the region of interest. Thus XAI based image classification is a robust prototype for automatic and transparent learning system while reducing the semantic gap between soft-computing and humans way of perception.

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A Deep Learning SAR Target Classification Experiment on MSTAR Dataset

Cited by 42 publications

References 6 publications

Synthetic Aperture Radar SAR Image Target Recognition Algorithm Based on Attention Mechanism

Synthetic Aperture Radar SAR Image Target Recognition Algorithm Based on Attention Mechanism

Convolutional Extreme Learning Machines: A Systematic Review

Deep learning-based explainable target classification for synthetic aperture radar images

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