Special Issue "Recent Advances in Hyperspectral Image Processing"

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 December 2021.

Special Issue Editors

Prof. Dr. Liangpei Zhang
grade E-Mail Website
Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: pattern analysis and machine learning; image processing engineering; application of remote sensing; computational intelligence and its application in remote sensing image processing; application of remote sensing
Special Issues and Collections in MDPI journals
Prof. Dr. Lefei Zhang
E-Mail Website
Guest Editor
School of Computer Science, Wuhan University, Wuhan 430072, China
Interests: pattern recognition; machine learning; image processing; remote sensing
Special Issues and Collections in MDPI journals
Dr. Qian Shi
E-Mail Website
Guest Editor
School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
Interests: hyperspectral image classification; change detection; deep learning; urban land use mapping
Dr. Yanni Dong
E-Mail Website
Guest Editor
Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China
Interests: hyperspectral target detection; dimensionality reduction; scene classification; metric learning; transfer learning; multi-source remote sensing data geological interpretation

Special Issue Information

Dear Colleagues,

Over the past decades, hyperspectral imagery (HSI) has helped to observe and analyze various ground cover materials with abundant spectral information from hundreds or thousands of spectral bands. The rich spectral information provided by HSI makes it possible to distinguish various surface materials because every material has its own reflectance spectra characteristics, thus allowing for the application of HSI in many fields, including agriculture, forestry, environmental monitoring, geology, mineralogy, military, and medical imaging. Hyperspectral image processing techniques are developing rapidly in the current remote sensing community. Particularly, the development of computer technology and calculation techniques such as artificial intelligence, deep learning, and weakly supervised learning has expanded and enhanced the application direction and scope of hyperspectral image processing in recent years. However, several challenges and open problems are still waiting for efficient solutions and novel methodologies. The main goal of this Special Issue is to address advanced topics related to hyperspectral image processing.

This Special Issue is open to any researchers working on hyperspectral data applications and processing. Topics of interests include but are not limited to the following:

  • Radiative transfer modeling;
  • Fusion and resolution enhancement;
  • Spectral signature libraries and databases;
  • Denoising, restoration, and super resolution;
  • Endmember extraction and unmixing;
  • Dimensionality reduction and band selection;
  • Classification and segmentation;
  • Target and anomaly detection;
  • Change detection and time-series HSI analysis;
  • Artificial intelligence for HSI;
  • Mineral mapping, lithologic mapping, and geological applications;
  • Water quality monitoring;
  • Soil environment monitoring;
  • Forest species identification;
  • Wetland classification;
  • Vegetation health monitoring.

Prof. Dr. Liangpei Zhang
Dr. Lefei Zhang
Dr. Qian Shi
Dr. Yanni Dong
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 2400 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
  • Image processing
  • Machine learning
  • Pattern recognition

Published Papers (6 papers)

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Research

Article
Neighboring Discriminant Component Analysis for Asteroid Spectrum Classification
Remote Sens. 2021, 13(16), 3306; https://doi.org/10.3390/rs13163306 - 20 Aug 2021
Viewed by 390
Abstract
With the rapid development of aeronautic and deep space exploration technologies, a large number of high-resolution asteroid spectral data have been gathered, which can provide diagnostic information for identifying different categories of asteroids as well as their surface composition and mineralogical properties. However, [...] Read more.
With the rapid development of aeronautic and deep space exploration technologies, a large number of high-resolution asteroid spectral data have been gathered, which can provide diagnostic information for identifying different categories of asteroids as well as their surface composition and mineralogical properties. However, owing to the noise of observation systems and the ever-changing external observation environments, the observed asteroid spectral data always contain noise and outliers exhibiting indivisible pattern characteristics, which will bring great challenges to the precise classification of asteroids. In order to alleviate the problem and to improve the separability and classification accuracy for different kinds of asteroids, this paper presents a novel Neighboring Discriminant Component Analysis (NDCA) model for asteroid spectrum feature learning. The key motivation is to transform the asteroid spectral data from the observation space into a feature subspace wherein the negative effects of outliers and noise will be minimized while the key category-related valuable knowledge in asteroid spectral data can be well explored. The effectiveness of the proposed NDCA model is verified on real-world asteroid reflectance spectra measured over the wavelength range from 0.45 to 2.45 μm, and promising classification performance has been achieved by the NDCA model in combination with different classifier models, such as the nearest neighbor (NN), support vector machine (SVM) and extreme learning machine (ELM). Full article
(This article belongs to the Special Issue Recent Advances in Hyperspectral Image Processing)
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Article
Hyperspectral and Multispectral Image Fusion by Deep Neural Network in a Self-Supervised Manner
Remote Sens. 2021, 13(16), 3226; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163226 - 13 Aug 2021
Viewed by 466
Abstract
Compared with multispectral sensors, hyperspectral sensors obtain images with high- spectral resolution at the cost of spatial resolution, which constrains the further and precise application of hyperspectral images. An intelligent idea to obtain high-resolution hyperspectral images is hyperspectral and multispectral image fusion. In [...] Read more.
Compared with multispectral sensors, hyperspectral sensors obtain images with high- spectral resolution at the cost of spatial resolution, which constrains the further and precise application of hyperspectral images. An intelligent idea to obtain high-resolution hyperspectral images is hyperspectral and multispectral image fusion. In recent years, many studies have found that deep learning-based fusion methods outperform the traditional fusion methods due to the strong non-linear fitting ability of convolution neural network. However, the function of deep learning-based methods heavily depends on the size and quality of training dataset, constraining the application of deep learning under the situation where training dataset is not available or of low quality. In this paper, we introduce a novel fusion method, which operates in a self-supervised manner, to the task of hyperspectral and multispectral image fusion without training datasets. Our method proposes two constraints constructed by low-resolution hyperspectral images and fake high-resolution hyperspectral images obtained from a simple diffusion method. Several simulation and real-data experiments are conducted with several popular remote sensing hyperspectral data under the condition where training datasets are unavailable. Quantitative and qualitative results indicate that the proposed method outperforms those traditional methods by a large extent. Full article
(This article belongs to the Special Issue Recent Advances in Hyperspectral Image Processing)
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Article
A Novel Change Detection Approach Based on Spectral Unmixing from Stacked Multitemporal Remote Sensing Images with a Variability of Endmembers
Remote Sens. 2021, 13(13), 2550; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132550 - 29 Jun 2021
Viewed by 393
Abstract
Due to the high temporal repetition rates, median/low spatial resolution remote sensing images are the main data source of change detection (CD). It is worth noting that they contain a large number of mixed pixels, which makes adequately capturing the details in the [...] Read more.
Due to the high temporal repetition rates, median/low spatial resolution remote sensing images are the main data source of change detection (CD). It is worth noting that they contain a large number of mixed pixels, which makes adequately capturing the details in the resulting thematic map challenging. The spectral unmixing (SU) method is a potential solution to this problem, as it decomposes mixed pixels into a set of fractions of the land covers. However, there are accumulated errors in the fractional difference images, which lead to a poor change detection results. Meanwhile, the spectra variation of the endmember and the heterogeneity of the land cover materials cannot be fully considered in the traditional framework. In order to solve this problem, a novel change detection approach with image stacking and dividing based on spectral unmixing while considering the variability of endmembers (CD_SDSUVE) was proposed in this paper. Firstly, the remote sensing images at different times were stacked into a unified framework. After that, several patch images were produced by dividing the stacked images so that the similar endmembers according to each land cover can be completely extracted and compared. Finally, the multiple endmember spectral mixture analysis (MESMA) is performed, and the abundant images were combined to produce the entire change detection thematic map. This proposed algorithm was implemented and compared to four relevant state-of-the-art methods on three experimental data, whereby the results confirmed that it effectively improved the accuracy. In the simulated data, the overall accuracy (OA) and Kappa coefficient values were 99.61% and 0.99. In the two real data, the maximum of OA were acquired with 93.26% and 80.85%, which gained 14.88% and 13.42% over the worst results at most. Meanwhile, the Kappa coefficient value was consistent with the OA. Full article
(This article belongs to the Special Issue Recent Advances in Hyperspectral Image Processing)
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Article
Remote Hyperspectral Imaging Acquisition and Characterization for Marine Litter Detection
Remote Sens. 2021, 13(13), 2536; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132536 - 29 Jun 2021
Cited by 1 | Viewed by 732
Abstract
This paper addresses the development of a remote hyperspectral imaging system for detection and characterization of marine litter concentrations in an oceanic environment. The work performed in this paper is the following: (i) an in-situ characterization was conducted in an outdoor laboratory environment [...] Read more.
This paper addresses the development of a remote hyperspectral imaging system for detection and characterization of marine litter concentrations in an oceanic environment. The work performed in this paper is the following: (i) an in-situ characterization was conducted in an outdoor laboratory environment with the hyperspectral imaging system to obtain the spatial and spectral response of a batch of marine litter samples; (ii) a real dataset hyperspectral image acquisition was performed using manned and unmanned aerial platforms, of artificial targets composed of the material analyzed in the laboratory; (iii) comparison of the results (spatial and spectral response) obtained in laboratory conditions with the remote observation data acquired during the dataset flights; (iv) implementation of two different supervised machine learning methods, namely Random Forest (RF) and Support Vector Machines (SVM), for marine litter artificial target detection based on previous training. Obtained results show a marine litter automated detection capability with a 70–80% precision rate of detection in all three targets, compared to ground-truth pixels, as well as recall rates over 50%. Full article
(This article belongs to the Special Issue Recent Advances in Hyperspectral Image Processing)
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Article
Dimensionality Reduction of Hyperspectral Image Based on Local Constrained Manifold Structure Collaborative Preserving Embedding
Remote Sens. 2021, 13(7), 1363; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071363 - 02 Apr 2021
Cited by 5 | Viewed by 636
Abstract
Graph learning is an effective dimensionality reduction (DR) manner to analyze the intrinsic properties of high dimensional data, it has been widely used in the fields of DR for hyperspectral image (HSI) data, but they ignore the collaborative relationship between sample pairs. In [...] Read more.
Graph learning is an effective dimensionality reduction (DR) manner to analyze the intrinsic properties of high dimensional data, it has been widely used in the fields of DR for hyperspectral image (HSI) data, but they ignore the collaborative relationship between sample pairs. In this paper, a novel supervised spectral DR method called local constrained manifold structure collaborative preserving embedding (LMSCPE) was proposed for HSI classification. At first, a novel local constrained collaborative representation (CR) model is designed based on the CR theory, which can obtain more effective collaborative coefficients to characterize the relationship between samples pairs. Then, an intraclass collaborative graph and an interclass collaborative graph are constructed to enhance the intraclass compactness and the interclass separability, and a local neighborhood graph is constructed to preserve the local neighborhood structure of HSI. Finally, an optimal objective function is designed to obtain a discriminant projection matrix, and the discriminative features of various land cover types can be obtained. LMSCPE can characterize the collaborative relationship between sample pairs and explore the intrinsic geometric structure in HSI. Experiments on three benchmark HSI data sets show that the proposed LMSCPE method is superior to the state-of-the-art DR methods for HSI classification. Full article
(This article belongs to the Special Issue Recent Advances in Hyperspectral Image Processing)
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Article
Automatic Cotton Mapping Using Time Series of Sentinel-2 Images
Remote Sens. 2021, 13(7), 1355; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071355 - 01 Apr 2021
Viewed by 533
Abstract
Large-scale crop mapping is essential for agricultural management. Phenological variation often exists in the same crop due to different climatic regions or practice management, resulting in current classification models requiring sufficient training samples from different regions. However, the cost of sample collection is [...] Read more.
Large-scale crop mapping is essential for agricultural management. Phenological variation often exists in the same crop due to different climatic regions or practice management, resulting in current classification models requiring sufficient training samples from different regions. However, the cost of sample collection is more time-consuming, costly, and labor-intensive, so it is necessary to develop automatic crop mapping models that require only a few samples and can be extended to a large area. In this study, a new white bolls index (WBI) based on the unique canopy of cotton at the bolls opening stage was proposed, which can characterize the intensity of bolls opening. The value of WBI will increase as the opening of the bolls increases. As a result, the white bolls index can be used to detect cotton automatically from other crops. Four study areas in different regions were used to evaluate the WBI performance. The overall accuracy (OA) for the four study sites was more than 82%. Additionally, the dates when the opening stage of bolls begins can be determined based on the time series of WBI. The results of this research demonstrated the potential of the proposed approach for cotton mapping using sentinel-2 time series of remotely sensed data. Full article
(This article belongs to the Special Issue Recent Advances in Hyperspectral Image Processing)
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