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Feature Extraction and Data Classification in Hyperspectral Imaging II

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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 August 2024 | Viewed by 5455

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Special Issue Editors

Dr. Jaime Zabalza

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Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow G1 1XW, UK
Interests: signal and image processing; hyperspectral imaging; remote sensing; data mining; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Jinchang Ren

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Guest Editor

National Subsea Centre & School of Computing, Robert Gordon University, Aberdeen, UK
Interests: computer vision; machine learning; big data analytics; hyperspectral imaging; nondestructive inspection
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Dr. Yijun Yan

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Guest Editor

National Subsea Centre, Robert Gordon University, Aberdeen, UK
Interests: signal and image processing; hyperspectral imaging; remote sensing; data mining; machine learning; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Hyperspectral remote sensing 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 for remote sensing applications.

This Special Issue (Volume 2) is open to any researcher working on hyperspectral remote sensing 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;
Deep learning approaches for data mining and data classification;
Visualisation of the data and features;
Fast implementation of the algorithms using a GPU, etc.;
Emerging new datasets and applications.

Dr. Jaime Zabalza
Prof. 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 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

hyperspectral remote sensing
feature extraction
dimensionality reduction
classification
deep learning
efficient computation

Published Papers (4 papers)

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Research

24 pages, 9639 KiB

Open AccessArticle

A Novel Semantic Content-Based Retrieval System for Hyperspectral Remote Sensing Imagery

by Fatih Ömrüuzun, Yasemin Yardımcı Çetin, Uğur Murat Leloğlu and Begüm Demir

Remote Sens. 2024, 16(8), 1462; https://doi.org/10.3390/rs16081462 - 20 Apr 2024

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Abstract

With the growing use of hyperspectral remote sensing payloads, there has been a significant increase in the number of hyperspectral remote sensing image archives, leading to a massive amount of collected data. This highlights the need for an efficient content-based hyperspectral image retrieval (CBHIR) system to manage and enable better use of hyperspectral remote-sensing image archives. Conventional CBHIR systems characterize each image by a set of endmembers and then perform image retrieval based on pairwise distance measures. Such an approach significantly increases the computational complexity of the retrieval, mainly when the diversity of materials is high. Those systems also have difficulties in retrieving images containing particular materials with extremely low abundance compared to other materials, which leads to describing image content with inappropriate and/or insufficient spectral features. In this article, a novel CBHIR system to define global hyperspectral image representations based on a semantic approach to differentiate foreground and background image content for different retrieval scenarios is introduced to address these issues. The experiments conducted on a new benchmark archive of multi-label hyperspectral images, which is first introduced in this study, validate the retrieval accuracy and effectiveness of the proposed system. Comparative performance analysis with the state-of-the-art CBHIR systems demonstrates that modeling hyperspectral image content with foreground and background vocabularies has a positive effect on retrieval performance. Full article

(This article belongs to the Special Issue Feature Extraction and Data Classification in Hyperspectral Imaging II)

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22 pages, 7201 KiB

Open AccessArticle

Hyperspectral Image Classification Based on Mutually Guided Image Filtering

by Ying Zhan, Dan Hu, Xianchuan Yu and Yufeng Wang

Remote Sens. 2024, 16(5), 870; https://doi.org/10.3390/rs16050870 - 29 Feb 2024

Viewed by 613

Abstract

Hyperspectral remote sensing images (HSIs) have both spectral and spatial characteristics. The adept exploitation of these attributes is central to enhancing the classification accuracy of HSIs. In order to effectively utilize spatial and spectral features to classify HSIs, this paper proposes a method for the spatial feature extraction of HSIs based on a mutually guided image filter (muGIF) and combined with the band-distance-grouped principal component. Firstly, aiming at the problem that previously guided image filtering cannot effectively deal with the inconsistent information structure between the guided and target information, a method for extracting spatial features using muGIF is proposed. Then, aiming at the problem of the information loss caused by a single principal component as a guided image in the traditional GIF-based spatial–spectral classification, a spatial feature-extraction framework based on the band-distance-grouped principal component is proposed. The method groups the bands according to the band distance and extracts the principal components of each set of band subsets as the guide map of the current band subset to filter the HSIs. A deep convolutional neural network model and a generative adversarial network model for the filtered HSIs are constructed and then trained using samples for HSIs’ spatial–spectral classification. Experiments show that compared with the traditional methods and several popular spatial–spectral HSI classification methods based on a filter, the proposed methods based on muGIF can effectively extract the spatial–spectral features and improve the classification accuracy of HSIs. Full article

(This article belongs to the Special Issue Feature Extraction and Data Classification in Hyperspectral Imaging II)

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18 pages, 8283 KiB

Open AccessArticle

H-RNet: Hybrid Relation Network for Few-Shot Learning-Based Hyperspectral Image Classification

by Xiaoyong Liu, Ziyang Dong, Huihui Li, Jinchang Ren, Huimin Zhao, Hao Li, Weiqi Chen and Zhanhao Xiao

Remote Sens. 2023, 15(10), 2497; https://doi.org/10.3390/rs15102497 - 09 May 2023

Viewed by 1846

Abstract

Deep network models rely on sufficient training samples to perform reasonably well, which has inevitably constrained their application in classification of hyperspectral images (HSIs) due to the limited availability of labeled data. To tackle this particular challenge, we propose a hybrid relation network, H-RNet, by combining three-dimensional (3-D) convolution neural networks (CNN) and two-dimensional (2-D) CNN to extract the spectral–spatial features whilst reducing the complexity of the network. In an end-to-end relation learning module, the sample pairing approach can effectively alleviate the problem of few labeled samples and learn correlations between samples more accurately for more effective classification. Experimental results on three publicly available datasets have fully demonstrated the superior performance of the proposed model in comparison to a few state-of-the-art methods. Full article

(This article belongs to the Special Issue Feature Extraction and Data Classification in Hyperspectral Imaging II)

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20 pages, 1312 KiB

Open AccessArticle

Shadow Enhancement Using 2D Dynamic Stochastic Resonance for Hyperspectral Image Classification

by Qiuyue Liu, Min Fu and Xuefeng Liu

Remote Sens. 2023, 15(7), 1820; https://doi.org/10.3390/rs15071820 - 29 Mar 2023

Cited by 2 | Viewed by 1171

Abstract

With the development of remote sensing technology, classification has become a meaningful way to explore the rich information in hyperspectral images (HSIs). However, various environmental factors may cause noise and shadow areas in HSIs, resulting in weak signals and difficulties in fully utilizing information. In addition, classification methods based on deep learning have made considerable progress, but features extracted from most networks have much redundancy. Therefore, a method based on two-dimensional dynamic stochastic resonance (2D DSR) shadow enhancement and convolutional neural network (CNN) classification combined with an attention mechanism (AM) for HSIs is proposed in this paper. Firstly, to protect the spatial correlation of HSIs, an iterative equation of 2D DSR based on the pixel neighborhood relationship was derived, which made it possible to perform matrix SR in the spatial dimension of the image, instead of one-dimensional vector resonance. Secondly, by using the noise in the shadow area to generate resonance, 2D DSR can help increase the signals in the shadow regions by preserving the spatial characteristics, and enhanced HSIs can be obtained. Then, a 3DCNN embedded with two efficient channel attention (ECA) modules and one convolutional block attention module (CBAM) was designed to make the most of critical features that significantly affect the classification accuracy by giving different weights. Finally, the performance of the proposed method was evaluated on a real-world HSI, and comparative studies were carried out. The experimental results showed that the proposed approach has promising prospects in HSIs’ shadow enhancement and information mining. Full article

(This article belongs to the Special Issue Feature Extraction and Data Classification in Hyperspectral Imaging II)

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