Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.9
5.0
Journals
Remote Sensing
Special Issues
Kernel-Based Remote Sensing Image Analysis
remotesensing-logo
Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

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 1750

Special Issue Editors

Dr. Saeid Niazmardi

E-Mail Website
Guest Editor
Department of Surveying Engineering, Faculty of Civil and Surveying Engineering, Graduate University of Advanced Technology, Kerman, Iran
Interests: remote sensing; image analysis; kernel-based machine learning; Neural network; environmental applications of remote sensing data
Dr. Reza Shah-Hosseini

E-Mail Website1 Website2
Guest Editor
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1417466191, Iran
Interests: autonomous aerial vehicles; crop mapping and monitoring; geophysical image processing; learning (artificial intelligence)
Special Issues, Collections and Topics in MDPI journals
Dr. Mahdi Hasanlou

E-Mail Website
Guest Editor
School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Interests: remote sensing; information systems; computer science applications; ecological modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Saeid Homayouni

E-Mail Website
Guest Editor
Institut National de la Recherche Scientifique (INRS), Centre Eau Terre Environnement, 490, rue de la Couronne, Québec, QC G1K 9A9, Canada
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

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.

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

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 11259 KiB  
Article
Improving Feature Learning in Remote Sensing Images Using an Integrated Deep Multi-Scale 3D/2D Convolutional Network
by Haron C. Tinega, Enqing Chen and Divinah O. Nyasaka
Remote Sens. 2023, 15(13), 3270; https://doi.org/10.3390/rs15133270 - 25 Jun 2023
Cited by 2 | Viewed by 944
Abstract
Developing complex hyperspectral image (HSI) sensors that capture high-resolution spatial information and voluminous (hundreds) spectral bands of the earth’s surface has made HSI pixel-wise classification a reality. The 3D-CNN has become the preferred HSI pixel-wise classification approach because of its ability to extract [...] Read more.
Developing complex hyperspectral image (HSI) sensors that capture high-resolution spatial information and voluminous (hundreds) spectral bands of the earth’s surface has made HSI pixel-wise classification a reality. The 3D-CNN has become the preferred HSI pixel-wise classification approach because of its ability to extract discriminative spectral and spatial information while maintaining data integrity. However, HSI datasets are characterized by high nonlinearity, voluminous spectral features, and limited training sample data. Therefore, developing deep HSI classification methods that purely utilize 3D-CNNs in their network structure often results in computationally expensive models prone to overfitting when the model depth increases. In this regard, this paper proposes an integrated deep multi-scale 3D/2D convolutional network block (MiCB) for simultaneous low-level spectral and high-level spatial feature extraction, which can optimally train on limited sample data. The strength of the proposed MiCB model solely lies in the innovative arrangement of convolution layers, giving the network the ability (i) to simultaneously convolve the low-level spectral with high-level spatial features; (ii) to use multiscale kernels to extract abundant contextual information; (iii) to apply residual connections to solve the degradation problem when the model depth increases beyond the threshold; and (iv) to utilize depthwise separable convolutions in its network structure to address the computational cost of the proposed MiCB model. We evaluate the efficacy of our proposed MiCB model using three publicly accessible HSI benchmarking datasets: Salinas Scene (SA), Indian Pines (IP), and the University of Pavia (UP). When trained on small amounts of training sample data, MiCB is better at classifying than the state-of-the-art methods used for comparison. For instance, the MiCB achieves a high overall classification accuracy of 97.35%, 98.29%, and 99.20% when trained on 5% IP, 1% UP, and 1% SA data, respectively. Full article
(This article belongs to the Special Issue Kernel-Based Remote Sensing Image Analysis)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop