{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T13:50:39Z","timestamp":1784296239142,"version":"3.55.0"},"reference-count":28,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T00:00:00Z","timestamp":1681689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61971324"],"award-info":[{"award-number":["61971324"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61525105"],"award-info":[{"award-number":["61525105"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Target recognition is the core application of radar image interpretation. In recent years, deep learning has become the mainstream solution. However, this family of methods is highly dependent on a great deal of training samples. Limited samples may lead to problems such as underfitting and poor robustness. To solve the problem, numerous generative models have been presented. The generated samples played an important role in target recognition. It is therefore needed to assess the quality of simulated images. However, few studies were performed in the preceding works. To fill the gap, a new evaluation strategy is proposed in this paper. The proposed method is composed of two schemes: the sample-wise assessment and the class-wise one. The simulated images can then be evaluated from two different perspectives. The sample-wise assessment combines the Fisher separability criterion, fuzzy comprehensive evaluation, analytic hierarchy process, and image feature extraction into a unified framework. It is used to evaluate whether the relative intensity of the speckle noise of the SAR image and the target backscattering coefficients are well simulated. Contrarily, the class-wise assessment is designed to compare the application capability of the simulated images holistically. Multiple comparative experiments are performed to verify the proposed method.<\/jats:p>","DOI":"10.3390\/rs15082110","type":"journal-article","created":{"date-parts":[[2023,4,17]],"date-time":"2023-04-17T04:45:32Z","timestamp":1681706732000},"page":"2110","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["SAR Image Quality Assessment: From Sample-Wise to Class-Wise"],"prefix":"10.3390","volume":"15","author":[{"given":"Ziyi","family":"Yu","sequence":"first","affiliation":[{"name":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1527-2426","authenticated-orcid":false,"given":"Ganggang","family":"Dong","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongwei","family":"Liu","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"153391","DOI":"10.1109\/ACCESS.2019.2948618","article-title":"SAR Target Recognition Based on Cross-Domain and Cross-Task Transfer Learning","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xu, Q., Huang, G., Yuan, Y., Guo, C., Sun, Y., Wu, F., and Weinberger, K. (2018). An empirical study on evaluation metrics of generative adversarial networks. arXiv.","DOI":"10.1109\/BigData.2018.8622525"},{"key":"ref_3","first-page":"6629","article-title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium","volume":"30","author":"Heusel","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"27003","DOI":"10.1109\/ACCESS.2021.3057455","article-title":"SAR Image Generation of Ground Targets for Automatic Target Recognition Using Indirect Information","volume":"9","author":"Yoo","year":"2021","journal-title":"IEEE Access"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Nie, C., Kong, Y., Leung, H., and Yan, S. (2016, January 10\u201313). Evaluation methods of similarity between simulated SAR images and real SAR images. Proceedings of the 2016 CIE International Conference on Radar (RADAR), Guangzhou, China.","DOI":"10.1109\/RADAR.2016.8059495"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Yu, Z., and Dong, G. (2022, January 17\u201322). A New Quantitative Evaluation Strategy for Deep Generated Sar Images. Proceedings of the IGARSS 2022\u20142022 IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia.","DOI":"10.1109\/IGARSS46834.2022.9884006"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.2992429","article-title":"Parameter Extraction Based on Deep Neural Network for SAR Target Simulation","volume":"58","author":"Niu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Commun. ACM"},{"key":"ref_10","unstructured":"Radford, A., Metz, L., and Chintala, S. (2015). Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv."},{"key":"ref_11","unstructured":"Tolstikhin, I., Bousquet, O., Gelly, S., and Schoelkopf, B. (2017). Wasserstein auto-encoders. arXiv."},{"key":"ref_12","unstructured":"Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., and Courville, A. (2017). Improved training of wasserstein gans. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"42255","DOI":"10.1109\/ACCESS.2019.2907728","article-title":"Image Data Augmentation for SAR Sensor via Generative Adversarial Nets","volume":"7","author":"Cui","year":"2019","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3495","DOI":"10.1109\/TGRS.2019.2957453","article-title":"LDGAN: A Synthetic Aperture Radar Image Generation Method for Automatic Target Recognition","volume":"58","author":"Cao","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Xie, D., Ma, J., Li, Y., and Liu, X. (2021, January 18\u201320). Data Augmentation of Sar Sensor Image via Information Maximizing Generative Adversarial Net. Proceedings of the 2021 IEEE 4th International Conference on Electronic Information and Communication Technology (ICEICT), Xi\u2019an, China.","DOI":"10.1109\/ICEICT53123.2021.9531250"},{"key":"ref_16","unstructured":"Kingma, D.P., and Welling, M. (2013). Auto-encoding variational bayes. arXiv."},{"key":"ref_17","unstructured":"Dinh, L., Krueger, D., and Bengio, Y. (2014). Nice: Non-linear independent components estimation. arXiv."},{"key":"ref_18","unstructured":"Dinh, L., Sohl-Dickstein, J., and Bengio, S. (2016). Density estimation using Real NVP. arXiv."},{"key":"ref_19","first-page":"10215","article-title":"Glow: Generative flow with invertible 1 \u00d7 1 convolutions","volume":"31","author":"Kingma","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_20","unstructured":"Yu, Z., and Dong, G. (September, January 29). Similarity Analysis of Simulated SAR Target Images. Proceedings of the 2022 30th European Signal Processing Conference (EU-SIPCO), Belgrade, Serbia."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Huo, W., Huang, Y., Pei, J., Liu, X., and Yang, J. (2016, January 10\u201315). Virtual SAR target image generation and similarity. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729231"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1109\/LGRS.2020.2967456","article-title":"DeepImaging: A Ground Moving Target Imaging Based on CNN for SAR-GMTI System","volume":"18","author":"Mu","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Oveis, A.H., Giusti, E., Ghio, S., and Martorella, M. (2021, January 8\u201314). CNN for Radial Velocity and Range Components Estimation of Ground Moving Targets in SAR. Proceedings of the 2021 IEEE Radar Conference (RadarConf21), Atlanta, GA, USA.","DOI":"10.1109\/RadarConf2147009.2021.9455155"},{"key":"ref_24","first-page":"39","article-title":"A SAR dataset for a TR development: The Synthetic and Measured Paired Labeled Experiment (SAMPLE)","volume":"Volume 10987","author":"Lewis","year":"2019","journal-title":"Algorithms for Synthetic Aperture Radar Imagery XXVI"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sugawara, Y., Shiota, S., and Kiya, H. (2018, January 7\u201310). Super-Resolution Using Convolutional Neural Networks without Any Checkerboard Artifacts. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451141"},{"key":"ref_26","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_27","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_28","unstructured":"Theis, L., van den Oord, A., and Bethge, M. (2015). A note on the evaluation of generative models. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/2110\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:17:11Z","timestamp":1760123831000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/8\/2110"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,17]]},"references-count":28,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2023,4]]}},"alternative-id":["rs15082110"],"URL":"https:\/\/doi.org\/10.3390\/rs15082110","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,17]]}}}