{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T18:13:19Z","timestamp":1775326399172,"version":"3.50.1"},"reference-count":27,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T00:00:00Z","timestamp":1724976000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Artificial intelligence (AI) has made remarkable progress in recent years in remote sensing applications, including environmental monitoring, crisis management, city planning, and agriculture. However, the critical challenge in utilizing AI models in real-world remote sensing applications is maintaining their robustness and reliability, particularly against adversarial attacks. In adversarial attacks, attackers manipulate benign data to create a perturbation to mislead AI models into predicting incorrect decisions, posing a catastrophic threat to the security of their applications, particularly in crucial decision-making contexts. These attacks pose a significant threat to the integrity and comprehensiveness of AI models in remote sensing applications, as they can lead to inaccurate decisions with substantial consequences. In this paper, we propose to develop an adversarial robustness technique that will ensure the AI model\u2019s accurate prediction in the presence of adversarial perturbation. In this work, we address these challenges by developing a better adversarial training approach using explainable AI method-guided features and data augmentation techniques to strengthen the AI model prediction in remote sensing data against adversarial attacks. The proposed approach achieved the best adversarial robustness against Project Gradient Descent (PGD) attacks in EuroSAT and AID datasets and showed transferability of robustness against unseen attacks.<\/jats:p>","DOI":"10.3390\/rs16173210","type":"journal-article","created":{"date-parts":[[2024,8,30]],"date-time":"2024-08-30T07:45:47Z","timestamp":1725003947000},"page":"3210","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Improve Adversarial Robustness of AI Models in Remote Sensing via Data-Augmentation and Explainable-AI Methods"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-4451-1572","authenticated-orcid":false,"given":"Sumaiya","family":"Tasneem","sequence":"first","affiliation":[{"name":"Department of Computer Science, Kennesaw State University, Marietta, GA 30060, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9320-0858","authenticated-orcid":false,"given":"Kazi Aminul","family":"Islam","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Kennesaw State University, Marietta, GA 30060, USA"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,30]]},"reference":[{"key":"ref_1","first-page":"1747","article-title":"Remote sensing applications: An overview","volume":"93","author":"Navalgund","year":"2007","journal-title":"Curr. Sci."},{"key":"ref_2","first-page":"1609","article-title":"Remote sensing for natural disaster management","volume":"33","year":"2000","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"347","DOI":"10.18280\/ts.370301","article-title":"UC-Merced Image Classification with CNN Feature Reduction Using Wavelet Entropy Optimized with Genetic Algorithm","volume":"37","author":"Sert","year":"2020","journal-title":"Trait. Signal"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2217","DOI":"10.1109\/JSTARS.2019.2918242","article-title":"EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification","volume":"12","author":"Helber","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chan-Hon-Tong, A., Lenczner, G., and Plyer, A. (2021, January 11\u201316). Demotivate adversarial defense in remote sensing. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9554767"},{"key":"ref_7","unstructured":"Chen, L., Zhu, G., Li, Q., and Li, H. (2019). Adversarial example in remote sensing image recognition. arXiv."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1604","DOI":"10.1109\/TGRS.2020.2999962","article-title":"Assessing the threat of adversarial examples on deep neural networks for remote sensing scene classification: Attacks and defenses","volume":"59","author":"Xu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","unstructured":"Goodfellow, I., Shlens, J., and Szegedy, C. (2015, January 7\u20139). Explaining and Harnessing Adversarial Examples. Proceedings of the International Conference on Learning Representations, San Diego, CA, USA."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Kurakin, A., Goodfellow, I., and Bengio, S. (2017). Adversarial examples in the physical world. arXiv.","DOI":"10.1201\/9781351251389-8"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Carlini, N., and Wagner, D. (2017, January 22\u201326). Towards evaluating the robustness of neural networks. Proceedings of the 2017 IEEE Symposium on Security and Privacy, San Jose, CA, USA.","DOI":"10.1109\/SP.2017.49"},{"key":"ref_12","unstructured":"Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A. (2017). Towards Deep Learning Models Resistant to Adversarial Attacks. arXiv."},{"key":"ref_13","first-page":"1","article-title":"Perturbation-seeking generative adversarial networks: A defense framework for remote sensing image scene classification","volume":"60","author":"Cheng","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","unstructured":"Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R. (2013). Intriguing properties of neural networks. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, Y., Qi, J., Bin, K., Wen, H., Tong, X., and Zhong, P. (2022). Adversarial patch attack on multi-scale object detection for uav remote sensing images. Remote Sens., 14.","DOI":"10.20944\/preprints202210.0131.v1"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization","volume":"128","author":"Selvaraju","year":"2019","journal-title":"Int. J. Comput. Vis."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, H., Wang, Z., Du, M., Yang, F., Zhang, Z., Ding, S., Mardziel, P., and Hu, X. (2020). Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks. arXiv.","DOI":"10.1109\/CVPRW50498.2020.00020"},{"key":"ref_18","unstructured":"Dombrowski, A.K., Alber, M., Anders, C., Ackermann, M., M\u00fcller, K.R., and Kessel, P. (2019). Explanations can be manipulated and geometry is to blame. arXiv."},{"key":"ref_19","unstructured":"Chen, J., Wu, X., Rastogi, V., Liang, Y., and Jha, S. (2019). Robust attribution regularization. arXiv."},{"key":"ref_20","unstructured":"Boopathy, A., Liu, S., Zhang, G., Liu, C., Chen, P.Y., Chang, S., and Daniel, L. (2020). Proper network interpretability helps adversarial robustness in classification. arXiv."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.1109\/TGRS.2017.2685945","article-title":"AID: A benchmark data set for performance evaluation of aerial scene classification","volume":"55","author":"Xia","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (2016). Learning Deep Features for Discriminative Localization. arXiv.","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref_23","unstructured":"Uddin, A., Monira, M., Shin, W., Chung, T., and Bae, S.H. (2020). Saliencymix: A saliency guided data augmentation strategy for better regularization. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_25","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on Machine Learning (ICML-10), Haifa, Israel."},{"key":"ref_26","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_27","unstructured":"Brown, T.B., Man\u00e9, D., Roy, A., Abadi, M., and Gilmer, J. (2018). Adversarial Patch. arXiv."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3210\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:45:44Z","timestamp":1760111144000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/17\/3210"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,30]]},"references-count":27,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["rs16173210"],"URL":"https:\/\/doi.org\/10.3390\/rs16173210","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,30]]}}}