Adversarial Attacks and Defenses for Remote Sensing Data

Dear Colleagues,

Security and reliability are important factors when addressing geoscience and remote sensing tasks. While artificial intelligence (AI) techniques, especially deep learning algorithms, have significantly improved the interpretation performance of remote sensing data in the past few years, recent research shows there exist some potential risks that these techniques may get attacked by specific deception algorithms. Such deception algorithms are known as "adversarial attacks", which can generate subtle perturbations that are imperceptible to a human observer but may greatly mislead the state-of-the-art deep learning methods to make wrong predictions.

To tackle this challenge and boost the development of secure AI algorithms in the remote sensing field, we would like to invite you to contribute to this Special Issue, which will gather new insights and contributions to the study of Adversarial Attacks and Defenses for Remote Sensing Data. Original research articles and reviews are welcome. Topics can be related but not limited to:

• Adversarial examples in hyperspectral/multispectral/RGB/LiDAR/synthetic aperture radar (SAR) data
• Adversarial attacks for scene classification, object detection, and semantic segmentation of remote sensing data
• Explainable adversarial examples in remote sensing data
• Black-box and white-box adversarial attacks
• Adversarial attacks in the physical world
• Advanced deep learning architectures with high resistance to adversarial examples
• Adversarial examples detection
• Adversarial defenses.

Dr. Yonghao Xu
Dr. Bo Du
Dr. Pedram Ghamisi 
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.