Special Issue "Enhanced Representation of High-Temporal-Resolution Remote Sensing Data"
Deadline for manuscript submissions: closed (31 July 2020).
Interests: edge computing; cloud computing; big data; Internet of Things
Interests: neuroengineering; data science; AI
Interests: internet of things; distributed computing; mobile and cloud computing
Special Issues and Collections in MDPI journals
Special Issue in Sensors: Smart IoT Sensing
Special Issue in Sensors: Real-Time AI over IoT Data
Special Issue in Sensors: Mobile Crowdsensing in Smart Cities
Special Issue in IoT: Mobile Computing for IoT
Special Issue in Remote Sensing: Internet of Things (IoT) Remote Sensing
At present, surveillance video technology plays a more and more important role in smart city applications. For example, an increasing number of low-earth-orbit satellites provide us with the opportunity to make frequent surveillance from space. An increasing number of surveillance cameras inside cities help us better monitor them continuously. These critical applications capture images and videos through constant observing (of a place). Such (remote) sensing data are routinely of a high-temporal resolution. The capability to analyse them has boosted numerous killer applications that were previously impossible, such as monitoring activities of factories on ports, monitoring the urban traffic flow, autopiloting and disaster management.
Taking RS data via satellites as an example, there is historical information accumulated of the currently captured place, possibly of variant resolutions, seasons and illumination. It can be beneficial to enhance the representation of the current data for more accurate analysis and efficient compression, and a priori information can help to make up for the current low quality of sensing data. Moreover, monitoring of a region with satellite videos demands real-time playback of the scene on Earth, but conflicts exist between the high bitrate of the dynamic data and the narrow satellite–Earth transmission bandwidth. The historical prior information can help to reduce the redundancy of the data for compressive representation of the data.
However, to make use of historical data for representation enhancement of the current data, challenges still remain on (I) how to eliminate inferential factors and model the prior knowledge from a large amount of historical data, (II) how to integrate prior knowledge with current data to enhance the representation, and (III) whether the prior knowledge model should be general for all applications or specific to different applications. Hence, in order to extract the benefits of the recently launched low cost satellites with high-frequency data, there is a pressing need to address the challenges in enhanced representation from historical data. Therefore, this Special Issue seeks original research papers that report on new approaches, methods, systems and solutions on the enhanced representation of high-frequency remotely sensed data.
Prof. Dr. Rajiv Ranjan
Prof. Dr. Dan Chen
Dr. Prem Prakash Jayaraman
Dr. Deepak Puthal
 Xue, Jie & Leung, Yee & Fung, Tung. "An Unmixing-Based Bayesian Model for Spatio-Temporal Satellite Image Fusion in Heterogeneous Landscapes". Remote Sensing，Vol. 11. 324-345, 2019.
 X. Wang, R. Hu, Z. Wang and J. Xiao, "Virtual Background Reference Frame Based Satellite Video Coding," in IEEE Signal Processing Letters, vol. 25, no. 10, pp. 1445-1449, 2018.
 Hongyang Lu & Jingbo Wei & Lizhe Wang & Peng Liu & Qiegen Liu & Yuhao Wang & Xiaohua Deng. "An Unmixing-Based Bayesian Model for Spatio-Temporal Satellite Image Fusion in Heterogeneous Landscapes". Remote Sensing，Vol. 8. 499-518, 2016.
Prof. Dr. Rajiv Ranjan
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.
- Modelling of prior knowledge
- Date integration
- Reference-based super resolution
- Data compression of high-temporal-resolution RS
- Historical data aided analysis
- Real-time scene understanding