Explainable Deep Neural Networks for Remote Sensing Image Understanding II

Dear Colleagues,

Thank you for your excellent responses to the first special volume.  In this second volume, we still maintain the focus on explainable deep neural Networks for remote sensing image understanding. Deep neural networks have been widely used in remote sensing image analysis and applications, e.g., classification, detection, regression, and inversion. Although these networks are somewhat successful in remote sensing image understanding, they still face a black-box problem, since both the feature extraction and classifier design are automatically learned. This problem seriously limits the development of deep learning and its applications in the field of remote sensing image understanding. In recent years, many explainable deep neural network models have been reported in the machine learning society, such as channel attention, spatial attention, self-attention, and Transformer network. These networks, to some extent, promote the development of explainable deep learning and address some important problems in remote sensing image analysis. On the other hand, remote sensing applications usually involve several exact physical models, e.g., the radiance transfer model, linear unmixing model, and spatiotemporal autocorrelation, which can also effectively model the formation process from the remote sensing data to land-cover observation and environmental parameter monitoring. However, how to effectively integrate popular deep neural networks with the traditional remote sensing physical models is currently the main challenge in remote sensing image understanding. Therefore, the research of theoretically and physically explainable deep convolutional neural networks is currently one of the most popular topics and can offer important advantages in the applications of remote sensing image understanding. This Special Issue aims to publish high-quality research papers and salient and informative review articles addressing emerging trends in remote sensing image understanding using the combination of explainable deep network and remote sensing physical models. Original contributions, not currently under review in a journal or a conference, are solicited in relevant areas including, but not limited to, the following:

• CNN/Transformernetworks for objection detection, segmentation, and recognition in remote sensing images.
• The hybrid architecture of CNN and Transformer for remote sensing image applications.
• Compact deep network models for remote sensing image applications.
• Physical model integrating with deep convolutional neural networks for remote sensing image applications.
• Hybrid models of joining data-driven and model-driven for remote sensing image applications.
• Incorporating geographical laws into deep convolutional neural networks for remote sensing image applications.
• Review/Surveys of remote sensing image processing.
• New remote sensing image datasets.

Dr. Tao Lei
Prof. Dr. Lefei Zhang
Dr. Tao Chen
Prof. Dr. Asoke K. Nandi
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 100 words) can be sent to the Editorial Office for announcement on this website.

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.

• deep learning
• remote sensing image analysis
• land-cover observation
• earth environmental monitoring
• attention mechanism
• data driven and model driven
• physical models