Special Issue "Artificial Intelligence and Machine Learning for multi-source Remote Sensing"
Deadline for manuscript submissions: closed (1 July 2023) | Viewed by 20488
Interests: remote sensing; data analysis; elaboration of satellite data for earth observation; machine learning applied to satellite images; sensor networks
Special Issues, Collections and Topics in MDPI journals
Special Issue in Remote Sensing: Applications and New Trends in Metrology for Radar/LiDAR-Based Systems
Special Issue in Geotechnics: Observing Strategic Infrastructure from Space
Special Issue in Remote Sensing: Applications and New Trends in Metrology for Radar/LiDAR-Based Systems II
Special Issue in Remote Sensing: Deep Neural Networks for Hyperspectral Remote Sensing Image Processing
Topics: Computer Vision and Image Processing
Interests: image processing; pattern; recognition; data science; IOT
Interests: statistical signal processing applied to radar target recognition global navigation satellite system reflectometry, and hyperspectral unmixing; elaboration of satellite data for Earth observation with application in imaging and sounding with passive (multispectral and hyperspectral) and active (SAR, GNSS-R) sensors
Special Issues, Collections and Topics in MDPI journals
Recently, artificial intelligence and machine learning have gained attention and have been used to achieve great success in both the research community and industry, especially in the field of multi-source remote sensing. Due to the recent progress in machine learning, particularly in deep learning, many techniques for image analysis and understanding have been applied to solve real problems, and deep learning has been widely used to perform other computer vision tasks, such as video classification and image super-resolution. Learning an effective feature representation from a large amount of data through artificial intelligence techniques is useful for extracting the underlying structural features of the data even when a small amount of data is available. It also results in a better representation than hand-crafted features as the features learned through artificial intelligence techniques adapt well to the tasks at hand.
Currently, massive streams of Earth Observation data are being systematically collected from different cutting-edge optical and radar sensors, on-board satellites, and aerial and terrestrial platforms. These data include both images and video sequences at different spatial, spectral, and temporal resolutions and can be used to constantly monitor the Earth's surface. In order to fully exploit these datasets and deliver crucial information for numerous engineering, environmental, safety, and security applications, novel artificial intelligence and machine learning methods are required that will enable us to efficiently dissect and interpret the data and draw conclusions that the broader public can turn into action.
The scope of this Special Issue is interdisciplinary and seeks collaborative contributions from academia and industrial experts in the areas of geoscience and remote sensing, signal processing, computer vision, machine learning, and data science.Prof. Dr. Silvia Liberata Ullo
Prof. Dr. Parameshachari Bidare Divakarachari
Prof. Dr. Pia Addabbo
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 2700 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.
- transfer learning and statistical learning methods for image classification
- multispectral imaging and deep neural networks for precision farming
- computer vision using deep convolutional networks for spatio-temporal remote sensing applications
- automatic building segmentation using multi-constraint convolutional networks
- urban land use mapping and analysis in the big data era
- deep learning techniques for remote sensing image classification
- detecting pipeline pathways in satellite images with deep learning
- marine vision-based situational awareness using deep learning
- computer vision for automatic ship detection in remote sensing images
- hyper-spectral image classification using similarity-measurement-based recurrent neural networks
- unsupervised deep feature extraction for remote sensing image classification