Special Issue "Image Retrieval in Remote Sensing"
Deadline for manuscript submissions: 31 December 2018
Dr. Erchan Aptoula
Institute of Information Technologies, Gebze Technical University, Gebze Technical University, Institute of Information Technologies, Cayirova Campus, 41400, Kocaeli, Turkey
Website | E-Mail
Interests: image analysis; mathematical morphology; content based image retrieval; hyperspectral imaging; deep learning
The continuous proliferation of Earth Observation satellites, along with their ever-increasing acquisition performances, has led to the formation of rapidly-growing geospatial data warehouses, in terms of both size and complexity, some of which are publicly available (e.g., Landsat, Sentinels). The analysis and exploration of such massive amounts of data has paved the way for various new applications, ranging from agricultural monitoring to crisis management and global security.
However, the rapid accumulation of gigabytes or terabytes worth of remote sensing data on a daily basis has rendered robust and automated tools, designed for their management, search, and retrieval, as essential for their effective exploitation. Of course, a variety of questions needs to be addressed, from the design of consistent and transferable data representations, to user-friendly querying and retrieval systems dealing with satellite images or mosaics. To this end, multiple methods have already been developed, mostly inspired from the multimedia context, by adapting the existing large body of knowledge in that domain.
Nevertheless, it has become fast clear that due to its much wider variety of sensors and resolutions, as well as the availability of rich prior knowledge, remote sensing retrieval encourages and often in fact renders it mandatory to go beyond mere adaptations and instead design original methods addressing effectively and efficiently these issues. The purpose of this Special Issue is to enable researchers from both multimedia retrieval and remote sensing to meet and share their experiences in order to build the remote sensing retrieval systems of tomorrow.
Topics of interest:
- Content- and context-based indexing, search and retrieval of RS data
- Search and browsing on RS Web repositories to face the Peta/Zettabyte scale
- Advanced descriptors and similarity metrics dedicated to RS data
- Usage of knowledge and semantic information for retrieval in RS
- Matching learning for image retrieval in remote sensing
- Query models, paradigms, and languages dedicated to RS
- Multimodal/multi-observations (sensors, dates, resolutions) analysis of RS data
- HCI issues in RS retrieval and browsing
- Evaluation of RS retrieval systems
- High performance indexing algorithms for RS data
- Real-time information retrieval techniques and applications
- Summarization and visualization of very large satellite image datasets
- Applications of image retrieval in remote sensing
The availability of various public remote sensing datasets, produced especially for content based retrieval and scene classification, has enabled objective and reproducible benchmarks among the plethora of published description, retrieval and classification methods. From the now relatively small sized UC Merced Land Use Dataset with 2100 aerial images to the large scale High Resolution EuroSat or Very High Resolution SpaceNet databases that cover millions of square meters, researchers are provided with sufficient means to propose and validate methods able to address a rich variety of use cases.
Prof. Sebastien Lefevre
Dr. Alexandre Benoit
Dr. Erchan Aptoula
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 monthly 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 1800 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.
- EO archives
- Content-based image retrieval
- Information querying and retrieval
- Remote sensing image indexing
- Big Data