Special Issue "Feature Extraction and Data Classification in Hyperspectral Imaging"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: 31 October 2020.

Special Issue Editors

Dr. Jaime Zabalza
Website
Guest Editor
University of Strathclyde, Glasgow, UK
Interests: signal and image processing; hyperspectral imaging; remote sensing; data mining; machine learning; artificial intelligence
Dr. Yijun Yan
Website
Guest Editor
University of Strathclyde, Glasgow, UK
Interests: signal and image processing; hyperspectral imaging; remote sensing; data mining; machine learning; artificial intelligence

Special Issue Information

Dear Colleagues,

Hyperspectral imaging is currently a fast-moving area of not only research but also industrial development, where captured hyperspectral cubes provide abundant information with great potential in many different applications. In this Special Issue, we aim to compile state-of-the-art research on how to tackle the “big data” problem of extracting the most useful information out of the hyperspectral paradigm.

This Special Issue is open to any researcher working on hyperspectral data mining and data classification. Specific topics include (but are not limited to) the following:

  • Denoising and enhancement;
  • Band selection and data reduction;
  • Supervised and unsupervised feature extraction and feature selection;
  • Compressive sensing and optimised data acquisition;
  • Spatial–spectral data fusion;
  • Spectral unmixing and super-resolution for improved classification;
  • Deep learning approaches for data mining and data classification;
  • Visualisation of the data and features;
  • Fast implementation of the algorithms using GPU, etc.;
  • Emerging new datasets and applications.
Dr. Jaime Zabalza
Dr. Jinchang Ren
Dr. Yijun Yan
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 papers will be 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 2200 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

  • Hyperspectral data
  • Feature extraction
  • Dimensionality reduction
  • Classification
  • Deep learning
  • Efficient computation

Published Papers (2 papers)

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Research

Open AccessArticle
Automatic Annotation of Hyperspectral Images and Spectral Signal Classification of People and Vehicles in Areas of Dense Vegetation with Deep Learning
Remote Sens. 2020, 12(13), 2111; https://doi.org/10.3390/rs12132111 - 01 Jul 2020
Abstract
Despite recent advances in image and video processing, the detection of people or cars in areas of dense vegetation is still challenging due to landscape, illumination changes and strong occlusion. In this paper, we address this problem with the use of a hyperspectral [...] Read more.
Despite recent advances in image and video processing, the detection of people or cars in areas of dense vegetation is still challenging due to landscape, illumination changes and strong occlusion. In this paper, we address this problem with the use of a hyperspectral camera—installed on the ground or possibly a drone—and detection based on spectral signatures. We introduce a novel automatic method for annotating spectral signatures based on a combination of state-of-the-art deep learning methods. After we collected millions of samples with our method, we used a deep learning approach to train a classifier to detect people and cars. Our results show that, based only on spectral signature classification, we can achieve an Matthews Correlation Coefficient of 0.83. We evaluate our classification method in areas with varying vegetation and discuss the limitations and constraints that the current hyperspectral imaging technology has. We conclude that spectral signature classification is possible with high accuracy in uncontrolled outdoor environments. Nevertheless, even with state-of-the-art compact passive hyperspectral imaging technology, high dynamic range of illumination and relatively low image resolution continue to pose major challenges when developing object detection algorithms for areas of dense vegetation. Full article
(This article belongs to the Special Issue Feature Extraction and Data Classification in Hyperspectral Imaging)
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Open AccessArticle
A Lightweight Spectral–Spatial Feature Extraction and Fusion Network for Hyperspectral Image Classification
Remote Sens. 2020, 12(9), 1395; https://doi.org/10.3390/rs12091395 - 28 Apr 2020
Abstract
Hyperspectral image (HSI) classification accuracy has been greatly improved by employing deep learning. The current research mainly focuses on how to build a deep network to improve the accuracy. However, these networks tend to be more complex and have more parameters, which makes [...] Read more.
Hyperspectral image (HSI) classification accuracy has been greatly improved by employing deep learning. The current research mainly focuses on how to build a deep network to improve the accuracy. However, these networks tend to be more complex and have more parameters, which makes the model difficult to train and easy to overfit. Therefore, we present a lightweight deep convolutional neural network (CNN) model called S2FEF-CNN. In this model, three S2FEF blocks are used for the joint spectral–spatial features extraction. Each S2FEF block uses 1D spectral convolution to extract spectral features and 2D spatial convolution to extract spatial features, respectively, and then fuses spectral and spatial features by multiplication. Instead of using the full connected layer, two pooling layers follow three blocks for dimension reduction, which further reduces the training parameters. We compared our method with some state-of-the-art HSI classification methods based on deep network on three commonly used hyperspectral datasets. The results show that our network can achieve a comparable classification accuracy with significantly reduced parameters compared to the above deep networks, which reflects its potential advantages in HSI classification. Full article
(This article belongs to the Special Issue Feature Extraction and Data Classification in Hyperspectral Imaging)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

1. Title: Hyperspectral Imaging of Semiconductor Defect Distributions and Movements using Automated Deconvolutions

Author List: Daram N. Ramdin1, Micah S. Haseman1, Hantian Gao1, Leonard J. Brillson1,2 

1 Department of Physics, The Ohio State University, Columbus, Ohio 43210, USA 

2 Department of Electrical and Computer Engineering, The Ohio State University, Columbus, Ohio 43210, USA

Abstract: Traditionally, cathodoluminescence-based hyperspectral imaging (HSI) has been predominantly used to identify the spatial position of different materials in a complex structure based on characteristic emissions. Recently, the spatial distribution of defects in ZnO has been imaged and correlated with electronic properties by using normalized intensity maps of a dominant spectral feature. The current work improves on this method by performing automated deconvolutions of HSI spectra, which allows for (1) the study of defect behavior to be done more precisely and (2) the study of materials where the defect energies are so closely spaced that a dominant spectral feature cannot be identified in advance. This paper aims to describe the new process by which defects are imaged and to review some of the results that have been attained using this new technique.

 

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