Kernel-Based Remote Sensing Image Analysis
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: closed (1 June 2023) | Viewed by 2182
Special Issue Editors
Interests: remote sensing; image analysis; kernel-based machine learning; Neural network; environmental applications of remote sensing data
Interests: autonomous aerial vehicles; crop mapping and monitoring; geophysical image processing; learning (artificial intelligence)
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing; information systems; computer science applications; ecological modeling
Special Issues, Collections and Topics in MDPI journals
Interests: analysis of optical; hyperspectral and radar Earth observations through artificial intelligence and machine-learning approaches for urban and agro-environmental applications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In the last decades, kernel-based algorithms have been considered standard machine learning tools for various remote sensing data analyses, such as classification, regression, detection, etc. Thanks to the well-known kernel trick, these algorithms can handle data non-linearities. Besides, several other characteristics of kernel-based algorithms, such as solid theoretical background, outstanding performances, and convex optimization, turn them into a proper choice for several remote sensing data analyses.
The literature on remote sensing contains a large number of research works that either proposed a new kernel-based algorithm for a specific application or evaluated the available algorithms for different data modalities. The most used and studied kernel-based algorithms are maximum margin classification algorithms, especially support vector machine (SVM) classifiers. Despite their prevalent use, several recent advances in kernel-based analyses have not been evaluated yet for remote sensing data. For instance, new kernel functions for various data modalities, multiple kernel learning, quantum kernels, and deep kernel learning are among these advancements applicable for remote sensing data analyses.
This special issue aims to promote and highlight the recent advances in kernel-based algorithms for remote sensing data analysis. We welcome submissions that provide the remote sensing community with the most recent developments in kernel-based algorithms' related aspects such as theory, development, applications, optimization, and improvement.
Dr. Saeid Niazmardi
Dr. Reza Shah-Hosseini
Dr. Mahdi Hasanlou
Dr. Saeid Homayouni
Guest Editors
Manuscript Submission Information
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Keywords
- kernel-based image classification and landcover mapping
- kernel-based feature selection and extraction
- kernel-based anomaly and target detection
- kernel-based change detection
- kernel-based domain adaptation
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