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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: closed (31 August 2024) | Viewed by 13657

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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

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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

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Published Papers (9 papers)

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Research

26 pages, 4756 KiB

Open AccessArticle

An Adaptive Unmixing Method Based on Iterative Multi-Objective Optimization for Surface Water Fraction Mapping (IMOSWFM) Using Zhuhai-1 Hyperspectral Images

by Cong Lei, Rong Liu, Zhiyuan Kuang and Ruru Deng

Remote Sens. 2024, 16(21), 4038; https://doi.org/10.3390/rs16214038 - 30 Oct 2024

Viewed by 400

Abstract

Surface water fraction mapping is an essential preprocessing step for the subpixel mapping (SPM) of surface water, providing valuable prior knowledge about surface water distribution at the subpixel level. In recent years, spectral mixture analysis (SMA) has been extensively applied to estimate surface water fractions in multispectral images by decomposing each mixed pixel into endmembers and their corresponding fractions using linear or nonlinear spectral mixture models. However, challenges emerge when introducing existing surface water fraction mapping methods to hyperspectral images (HSIs) due to insufficient exploration of spectral information. Additionally, inaccurate extraction of endmembers can result in unsatisfactory water fraction estimations. To address these issues, this paper proposes an adaptive unmixing method based on iterative multi-objective optimization for surface water fraction mapping (IMOSWFM) using Zhuhai-1 HSIs. In IMOSWFM, a modified normalized difference water fraction index (MNDWFI) was developed to fully exploit the spectral information. Furthermore, an iterative unmixing framework was adopted to dynamically extract high-quality endmembers and estimate their corresponding water fractions. Experimental results on the Zhuhai-1 HSIs from three test sites around Nanyi Lake indicate that water fraction maps obtained by IMOSWFM are closest to the reference maps compared with the other three SMA-based surface water fraction estimation methods, with the highest overall accuracy (OA) of 91.74%, 93.12%, and 89.73% in terms of pure water extraction and the lowest root-mean-square errors (RMSE) of 0.2506, 0.2403, and 0.2265 in terms of water fraction estimation. This research provides a reference for adapting existing surface water fraction mapping methods to HSIs. Full article

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

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19 pages, 44218 KiB

Open AccessArticle

Testing the Impact of Pansharpening Using PRISMA Hyperspectral Data: A Case Study Classifying Urban Trees in Naples, Italy

by Miriam Perretta, Gabriele Delogu, Cassandra Funsten, Alessio Patriarca, Eros Caputi and Lorenzo Boccia

Remote Sens. 2024, 16(19), 3730; https://doi.org/10.3390/rs16193730 - 8 Oct 2024

Viewed by 816

Abstract

Urban trees support vital ecological functions and help with the mitigation of and adaption to climate change. Yet, their monitoring and management require significant public resources. remote sensing could facilitate these tasks. Recent hyperspectral satellite programs such as PRISMA have enabled more advanced remote sensing applications, such as species classification. However, PRISMA data’s spatial resolution (30 m) could limit its utility in urban areas. Improving hyperspectral data resolution with pansharpening using the PRISMA coregistered panchromatic band (spatial resolution of 5 m) could solve this problem. This study addresses the need to improve hyperspectral data resolution and tests the pansharpening method by classifying exemplative urban tree species in Naples (Italy) using a convolutional neural network and a ground truths dataset, with the aim of comparing results from the original 30 m data to data refined to a 5 m resolution. An evaluation of accuracy metrics shows that pansharpening improves classification quality in dense urban areas with complex topography. In fact, pansharpened data led to significantly higher accuracy for all the examined species. Specifically, the Pinus pinea and Tilia x europaea classes showed an increase of 10% to 20% in their F1 scores. Pansharpening is seen as a practical solution to enhance PRISMA data usability in urban environments. Full article

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

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20 pages, 2672 KiB

Open AccessArticle

Low-Rank Discriminative Embedding Regression for Robust Feature Extraction of Hyperspectral Images via Weighted Schatten p-Norm Minimization

by Chen-Feng Long, Ya-Ru Li, Yang-Jun Deng, Wei-Ye Wang, Xing-Hui Zhu and Qian Du

Remote Sens. 2024, 16(16), 3081; https://doi.org/10.3390/rs16163081 - 21 Aug 2024

Viewed by 711

Abstract

Low-rank representation (LRR) is widely utilized in image feature extraction, as it can reveal the underlying correlation structure of data. However, the subspace learning methods based on LRR suffer from the problems of lacking robustness and discriminability. To address these issues, this paper proposes a new robust feature extraction method named the weighted Schatten p-norm minimization via low-rank discriminative embedding regression (WSNM-LRDER) method. This method works by integrating weighted Schatten p-norm and linear embedding regression into the LRR model. In WSNM-LRDER, the weighted Schatten p-norm is adopted to relax the low-rank function, which can discover the underlying structural information of the image, to enhance the robustness of projection learning. In order to improve the discriminability of the learned projection, an embedding regression regularization is constructed to make full use of prior information. The experimental results on three hyperspectral images datasets show that the proposed WSNM-LRDER achieves better performance than some advanced feature extraction methods. In particular, the proposed method yielded increases of more than 1.2%, 1.1%, and 2% in the overall accuracy (OA) for the Kennedy Space Center, Salinas, and Houston datasets, respectively, when comparing with the comparative methods. Full article

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

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19 pages, 2920 KiB

Open AccessArticle

SSA-LHCD: A Singular Spectrum Analysis-Driven Lightweight Network with 2-D Self-Attention for Hyperspectral Change Detection

by Yinhe Li, Jinchang Ren, Yijun Yan, Genyun Sun and Ping Ma

Remote Sens. 2024, 16(13), 2353; https://doi.org/10.3390/rs16132353 - 27 Jun 2024

Cited by 1 | Viewed by 822

Abstract

As an emerging research hotspot in contemporary remote sensing, hyperspectral change detection (HCD) has attracted increasing attention in remote sensing Earth observation, covering land mapping changes and anomaly detection. This is primarily attributable to the unique capacity of hyperspectral imagery (HSI) to amalgamate both the spectral and spatial information in the scene, facilitating a more exhaustive analysis and change detection on the Earth’s surface, proving to be successful across diverse domains, such as disaster monitoring and geological surveys. Although numerous HCD algorithms have been developed, most of them face three major challenges: (i) susceptibility to inherent data noise, (ii) inconsistent accuracy of detection, especially when dealing with multi-scale changes, and (iii) extensive hyperparameters and high computational costs. As such, we propose a singular spectrum analysis-driven-lightweight network for HCD, where three crucial components are incorporated to tackle these challenges. Firstly, singular spectrum analysis (SSA) is applied to alleviate the effect of noise. Next, a 2-D self-attention-based spatial–spectral feature-extraction module is employed to effectively handle multi-scale changes. Finally, a residual block-based module is designed to effectively extract the spectral features for efficiency. Comprehensive experiments on three publicly available datasets have fully validated the superiority of the proposed SSA-LHCD model over eight state-of-the-art HCD approaches, including four deep learning models. Full article

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

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25 pages, 25911 KiB

Open AccessArticle

Hyperspectral Anomaly Detection via Low-Rank Representation with Dual Graph Regularizations and Adaptive Dictionary

by Xi Cheng, Ruiqi Mu, Sheng Lin, Min Zhang and Hai Wang

Remote Sens. 2024, 16(11), 1837; https://doi.org/10.3390/rs16111837 - 21 May 2024

Cited by 1 | Viewed by 1255

Abstract

In a hyperspectral image, there is a close correlation between spectra and a certain degree of correlation in the pixel space. However, most existing low-rank representation (LRR) methods struggle to utilize these two characteristics simultaneously to detect anomalies. To address this challenge, a novel low-rank representation with dual graph regularization and an adaptive dictionary (DGRAD-LRR) is proposed for hyperspectral anomaly detection. To be specific, dual graph regularization, which combines spectral and spatial regularization, provides a new paradigm for LRR, and it can effectively preserve the local geometrical structure in the spectral and spatial information. To obtain a robust background dictionary, a novel adaptive dictionary strategy is utilized for the LRR model. In addition, extensive comparative experiments and an ablation study were conducted to demonstrate the superiority and practicality of the proposed DGRAD-LRR method. Full article

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

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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

Viewed by 1823

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

Cited by 2 | Viewed by 1380

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 - 9 May 2023

Cited by 2 | Viewed by 2406

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 3 | Viewed by 1640

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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