Kernel-Based Remote Sensing Image Analysis

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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