- Satellite images are humungous sources of data that require efficient methods for knowledge discovery. The increased availability of earth data from satellite images has immense opportunities in various fields. However, the volume and heterogeneity of data poses serious computational challenges. The development of efficient techniques has the potential of discovering hidden information from these images. This knowledge can be used in various activities related to planning, monitoring, and managing the earth resources. Deep learning are being widely used for image analysis and processing. Deep learning based models can be effectively used for mining and knowledge discovery from satellite images.
The heterogeneity and large volume of the satellite data requires efficient optimization methods. Evolutionary algorithms are being applied in various areas for optimization problems. The various areas that can benefit from satellite images are sustainable urban development, land cover use, and population density mapping, housing provision, traffic management, city infrastructure management, climate applications, agriculture mapping, change detection, disaster management. Thus this SI aims to bring together researchers to showcase their latest innovations. The SI will bring new research in the field of satellite image mining, analytics, and knowledge discovery. The focus is to build efficient technologies to improve the current scenario of satellite image analysis for various applications.
The topics of this special issue include, but are not limited to the following:
· Land cover use and classification
· Hybrid models for thematic mapping
· Deep learning for image understanding including semantic labelling, object detection, or image retrieval
· Transfer learning and domain adaptation for satellite images
· Deep learning models for climate predictions
· Dimensionality reduction of satellite images
· Models for urban, environmental, geological, and civilian applications
· Hyperspectral image processing and mining
· Change detection, time-series analysis for multi-temporal datasets
· Deep learning and evolutionary algorithms for remote sensing data processing (e.g., object or target detection, classification, parameter adaptation)
· Training and testing deep learning algorithms and solutions to remote sensing problems
· Deep learning for remote sensing data fusion
· Deep learning with scarce or low-quality remote sensing data across resolutions or sensors
· Deep learning for time-series applications
· Multi-sensor satellite image analysis using evolutionary algorithm
· Hybrid optimization algorithms for satellite image applications
· Deep learning for SAR image classification
The authors are requested to submit their full research papers complying with the general scope of the journal. The submitted papers will undergo peer review process before they can be accepted. Notification of acceptance will be communicated as we progress with the review process.
Papers submitted to this journal for possible publication must be original and must not be under consideration for publication in any other journals. Prospective authors should submit an electronic copy of their completed manuscript to https://mc03.manuscriptcentral.com/bdma with manuscript type as “Special Issue on Deep Learning and Evolutionary Computation for Satellite imagery”. Further information on the journal is available at: http://ieeexplore.ieee. org/xpl/RecentIssue.jsp?punumber=8254253.
Deadline for submissions: November 30, 2020
1st round of acceptance notification: January 15, 2021
Submission of revised papers: February 15, 2021
2nd round of acceptance notification: March 15, 2021
Publication online (tentative): April 15, 2021
Krishna Kant Singh, KIET Group of Institutions, India. E-mail: firstname.lastname@example.org
Soumyabrata Dev, University College Dublin, Ireland. E-mail: email@example.com
Akansha Singh, Amity University Uttar Pradesh, India. E-mail: firstname.lastname@example.org
Seungmin Rho, Sejong University, Korea. E-mail: email@example.com