English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Synthetic Aperture Radar SAR Automatic Target Recognition ATR is a keytechnique of remote-sensing image recognition which can be supported by deepneural networks The existing works of SAR ATR mostly focus on improving theaccuracy of the target recognition while ignoring the systems performance interms of speed and storage which is critical to real-world applications of SARATR For decision-makers aiming to identify a proper deep learning model todeploy in a SAR ATR system it is important to understand the performance ofdifferent candidate deep learning models and determine the best modelaccordingly This paper comprehensively benchmarks several advanced deeplearning models for SAR ATR with multiple distinct SAR imagery datasetsSpecifically we train and test five SAR image classifiers based on ResidualNeural Networks ResNet18 ResNet34 ResNet50 Graph Neural Network GNN and VisionTransformer for Small-Sized Datasets (SS-ViT) We select three datasets MSTARGBSAR and SynthWakeSAR that offer heterogeneity We evaluate and compare thefive classifiers concerning their classification accuracy runtime performancein terms of inference throughput and analytical performance in terms of numberof parameters number of layers model size and number of operations Experimentalresults show that the GNN classifier outperforms with respect to throughput andlatency However it is also shown that no clear model winner emerges from all ofour chosen metrics and a one model rules all case is doubtful in the domain ofSAR ATR

Benchmarking Deep Learning Classifiers for SAR Automatic Target Recognition