Special Issue "Lightweight Deep Neural Networks for Remote Sensing Image Understanding"
Deadline for manuscript submissions: closed (31 December 2020).
Interests: Computer vision; machine learning; remote sensing application
Interests: Artificial intelligence, machine learning
Interests: Remote sensing image processing; high-resolution remote sensing
Interests: hyperspectral image processing; artificial intelligence
Special Issues and Collections in MDPI journals
With the rapid development of intelligent information technology, images from remote sensing play a very important role in many research areas including geology, oceanography, weather forecasting, etc. However, compared to general digital images, remote sensing images require complex pre-processing such as optical geometric correction and radiation correction. Consequently, it is usually impossible to construct a dataset including a large number of remote sensing images. Deep learning techniques have emerged as a powerful alternative for machine learning with great model capacity and the learning ability of highly discriminative features for the task at hand. In particular, deep convolutional neural networks (CNN) have been widely used in the field of remote sensing. However, deep models often rely on a large number of annotated images, which is difficult for the field of remote sensing. How to train a lightweight deep learning model using small training samples is a challenge in remote sensing. This Special Issue aims to publish high-quality research papers, as well as review articles addressing emerging trends in remote sensing image understanding using lightweight deep neural network models. Original contributions, not currently under review for a journal or a conference, are solicited in relevant areas including, but not limited to, the following:
- Object detection in remote sensing images using lightweight deep neural networks
- Remote sensing image classification using lightweight deep neural networks
- Change detection using lightweight deep neural networks
- Super-resolution reconstruction of remote sensing images using lightweight deep neural networks
- Remote sensing image restoration using lightweight deep neural networks
- Remote sensing applications using deep neural networks
- Deep neural networks for hyperspectral data
- Review/Surveys of remote sensing image processing
- New remote sensing image datasets
Prof. Dr. Tao Lei
Dr. Hongying Meng
Dr. Shuying Li
Dr. Lefei Zhang
Dr. Jungong Han
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 papers will be 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 2400 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.
- remote sensing image classification
- remote sensing image restoration
- remote sensing application
- deep neural network
- hyperspectral image processing
- neural network compression