Deep Learning in Satellite Remote Sensing Applications
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: closed (30 October 2024) | Viewed by 11973
Special Issue Editor
Interests: applied mathematics; mathematical modeling in physics and engineering; optimal control; optimization; inverse problems; mathematical finance; numerical methods
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Satellite remote sensing applications are data-intensive, and the most common use of remote sensing is in monitoring activities. This means that in a data-intensive context, time series of data are widely available. Data science and in particular machine learning methods and algorithms are natural candidates to improve the performance of existing remote sensing applications or to develop new applications. Remote sensing sensors capture images across the electromagnetic spectrum, ranging from ultraviolet to infrared images. These images are taken in a variety of modes, ranging from single-band to hyperspectral images. Statistical methods have been used in the analysis of these images since the early days of remote sensing. Some of these methods include moving averages, linear regression, and principal component analysis. In recent times, new techniques coming from data science such as neural networks, convolutional neural networks, deep neural networks, support vector machines, and decision trees have received a lot of attention as tools to analyze images. Papers concerned with algorithmic innovations, data analysis, new hardware architectures involving machine learning methods and satellite remote sensing applications represent potential valuable contributions to this Special Issue. Speculative papers about future developments made possible by the emergence of new technologies (i.e., cloud computing, quantum computing, etc.) may also be published in this Special Issue.
Prof. Dr. Francesco Zirilli
Guest Editor
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Keywords
- machine learning
- regression analysis
- principal component analysis
- neural networks
- deep neural networks
- convolutional neural networks
- support vector machine
- decision tree
- remote sensing
- UV images
- IR images
- optical images
- single-band images
- multiband images
- hyperspectral images
- object detection
- feature extraction
- image classification
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