Submission deadline: 30 June 2020
Inspired by the great potential of human brains for object recognition, Deep Learning (DL) has drawn attention within the remote sensing community over the past few years. Supervised convolutional neural network (CNN), recurrent neural network (RNN), unsupervised Auto-Encoders (AE), deep belief network (DBN), and generative adversarial network (GANs) are the state-of-the-art DL methods applied for remote sensing imagery. Given a large enough training set, these methods are advantageous compared to conventional shallow structured machine learning tools, such as neural networks (NN), support vector machines (SVM), and ensemble algorithms, e.g., random forest (RF), which have been successfully used in the analysis of remotely sensed data for several years.
The popularity of DL methods is attributed to both their deep multilayer structure, allowing the extraction of robust, invariant, and high-level features of data, and to their end-to-end training scheme. In other words, these methods have the capability to learn a series of abstract hierarchical features from raw input data, and to provide a final, task-specific output, thus removing heuristic feature engineering design.
The Canadian Journal of Remote Sensing aims at publishing a special issue on “Deep Learning for Environmental Applications of Remote Sensing Data.” The main objective of this special issue is to promote the recent thematic research and development applications of deep learning approaches for a variety of remote sensing problems. Papers of applicative nature providing new deep learning-oriented public datasets for the remote sensing community are welcome.
Submissions are encouraged to cover a broad range of topics such as transfer learning, design of new deep learning architectures, efficient training of deep learning architectures, preparing large-scale deep learning-oriented public dataset, deep reinforcement learning, deep learning model in Geo Big Data, which may include, without being limited to, the following applications: image fusion, segmentation and classification; pan-sharpening, denoising and super-resolution; disaster responses (e.g., oil spill, inundation); land use/land cover change detection; target detection (e.g., ship and iceberg); crop yield prediction, and environmental monitoring (e.g., wetland and forest).
All manuscripts will be subjected to peer-review and standard publication fees. Research Papers, Review Papers, as well as Research Notes, are all welcome and invited.
Prospective authors must follow the regular guidelines of the CJRS-JCT available on the Taylor & Francis website at http://www.tandfonline.com/r/cjrs
The targeted publication date will be early 2021. Manuscripts must be submitted by 30 June 2020 via the CJRS-JCT ScholarOne Manuscripts website under the Special Issue on “Deep Learning for Environmental Applications of Remote Sensing Data,” at https://mc.manuscriptcentral.com/cjrs-jct/
Regular charges by paper ($400 USD for a research or review paper, $300 USD for a research note) will apply for all published papers, as well as the standard peer-review process.
CJRS-JCT Editor-in-Chief: Monique Bernier, INRS – Québec City, QC, Canada
Guest Editors – CJRS-JCT Special Issue on Deep Learning for Environmental Applications of Remote Sensing Data:
|Memorial U. / C-Core||St. John’s, NL, Canada|
|INRS||Quebec, Qc, Canada|
|CRIM||Montreal, Qc, Canada|