Special Issue "Analysis of Remote Sensing Image Data"
A special issue of Remote Sensing (ISSN 2072-4292).
Deadline for manuscript submissions: closed (15 January 2014) | Viewed by 69952
Interests: image analysis; image segmentation; pattern recognition and image analysis applications to remote sensing; massively parallel computation
Interests: remote sensing; image analysis; pattern recognition; signal processing
Special Issues, Collections and Topics in MDPI journals
Special Issue in Remote Sensing: Superpixel based Analysis and Classification of Remote Sensing Images
Special Issue in Remote Sensing: Multi-Modality Data Classification: Algorithms and Applications
Special Issue in Remote Sensing: Advanced Multisensor Image Analysis Techniques for Land-Cover Mapping
Special Issue in Sensors: UAV Imagery for Engineering Applications Using Artificial Intelligence Techniques (UAV-AI)
Special Issue in Remote Sensing: Advanced Machine Learning Techniques for High-Resolution Remote Sensing Data Analysis
Special Issue in Remote Sensing: Feature Papers for Remote Sensing Image Processing Section
Special Issue in Remote Sensing: Earth Observation Using Satellite Global Images of Remote Sensing
Computer-based analysis of remote sensing image data is of ever increasing importance as increasing volumes and types of digital image data become available from various aircraft and satellite based sensors. Effective utilization of this remote sensing image data requires an accurate extraction of the information contained in this data into terms relevant to the particular applications. And these applications are ever expanding, ranging from various land use and land cover mapping applications (e.g., monitoring urbanization, croplands, desertification, deforestation and forest health, glaciers and sea ice) to detecting and tracking air pollution and oil spills, to mineralogy other earth surface and atmospheric measurements.
With this special issue we compile state-of-the-art analysis methods for converting remote sensing image data into information relevant to various earth sciences and monitoring applications. We assume that the remote sensing image data has undergone radiometric and geometric correction processing. Analysis methods for image data collected and formed from optical, lidar and/or radar sensors are solicited. Review contributions are welcome as well as papers describing new analysis methods.
Dr. James C. Tilton
Prof. Dr. Jón Atli Benediktsson
Dr. Yuliya Tarabalka
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 submissions that pass pre-check are 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 2500 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.
- feature extraction
- image analysis
- image classification
- image edge detection
- image segmentation
- image texture
- object based image analysis
- pattern recognition
- machine learning
- mathematical morphology
- data fusion
- change detection