Special Issue "Widespread Applications Based on Hyperspectral Technologies from Space"

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 2020.

Special Issue Editors

Dr. Stefano Pignatti
E-Mail Website
Guest Editor
Institute of Methodologies for Environmental Analysis (IMAA), National Research Council (CNR), C.da S. Loja, 85050 Tito (PZ), Italy
Interests: hyperspectral remote sensing VSWIR-LWIR; sensor data calibration and pre-processing; field spectroscopy; retrieval of surfaces parameters; soil spectral characterization and geology; archaeological site analysis
Special Issues and Collections in MDPI journals
Dr. Fabrizia Buongiorno
E-Mail Website
Guest Editor
Istituto Nazionale di Geofisica e Vulcanologia Via di Vigna Murata 605 Rome, 00143, Italy
Interests: airborne and space imaging spectrometers acquiring data in the VSWIR-LWIR; technical characteristics and requirements for geophysical; geological applications; retrieval algorithms for surface temperature and volcanic gas emissions; space and ground data integration for Cultural Heritage preservation
Special Issues and Collections in MDPI journals
Prof. Bing Zhang
E-Mail Website
Guest Editor
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Interests: hyperspectral remote sensing; dynamic monitoring of global resource environment remote sensing
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Hyperspectral spaceborne missions have been acquiring images for over a decade, with hundreds of contiguous spectral (from VSWIR to LWIR) bands worldwide, supporting the development of a wide range of environmental applications.

Hyperion, Chris, HJ-1A and others, have highlighted both criticalities and the opportunities offered by the use of spaceborne hyperspectral technologies in the development of environmental products.

From 2017, a significant number of new orbit missions (such as GF-5, EnMAP, PRISMA, CCRSS, ECOSTRESS) will become available, giving scientists the new challenging scenario of a hyperspectral sensor constellation acquiring data at global scale with a reduced time frequency.

Moreover, according to scientific literature, more complex hyperspectral missions are under development/study (e.g., HYSPIRI, HISUI, HYPXIM, Shalom).

The aim of this Special Issue is to highlight the impact of the past hyperspectral missions and foresee the effectiveness of the future ones. The Special Issue will reflect on the experiences learnt in the past and present missions and perspectives potentially offered by the advent of the new ones. This could be achieved by describing how the retrieval of surface parameters and the understanding of surface phenomena can be enhanced by the availability of the new hyperspectral spaceborne missions.

Some scientific challenges relate to the development of land surface (including coastal systems) products that will benefit from the upcoming hyperspectral resources, especially when combined with available EO data.

Therefore, we would like to invite submissions on the following topics:

  • Integration and comparison of new hyperspectral image data/constellation;
  • Natural processes and human activities and their interactions, including archaeology;
  • Environmental and natural hazards and risks reduction;
  • Coastal systems, including inlands waters, and their interaction with the land;
  • Geology, soil and agriculture;
  • Atmospheric correction and atmospheric constituent characterization;
  • Hyperspectral data processing for defence and security;
  • Astrophysics and planetary exploration;
  • Hyperspectral sensors synergy with the other missions;
  • Sensor calibration including vicarious calibration.

Authors are required to check and follow the specific Instructions to Authors, https://www.mdpi.com/journal/remotesensing/instructions.

Dr. Stefano Pignatti
Dr. Maria Fabrizia Buongiorno
Dr. Bing Zhang
Guest Editors

Published Papers (6 papers)

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Research

Open AccessArticle
A Novel Tri-Training Technique for the Semi-Supervised Classification of Hyperspectral Images Based on Regularized Local Discriminant Embedding Feature Extraction
Remote Sens. 2019, 11(6), 654; https://doi.org/10.3390/rs11060654 - 18 Mar 2019
Cited by 1
Abstract
This paper introduces a novel semi-supervised tri-training classification algorithm based on regularized local discriminant embedding (RLDE) for hyperspectral imagery. In this algorithm, the RLDE method is used for optimal feature information extraction, to solve the problems of singular values and over-fitting, [...] Read more.
This paper introduces a novel semi-supervised tri-training classification algorithm based on regularized local discriminant embedding (RLDE) for hyperspectral imagery. In this algorithm, the RLDE method is used for optimal feature information extraction, to solve the problems of singular values and over-fitting, which are the main problems in the local discriminant embedding (LDE) and local Fisher discriminant analysis (LFDA) methods. An active learning method is then used to select the most useful and informative samples from the candidate set. In the experiments undertaken in this study, the three base classifiers were multinomial logistic regression (MLR), k-nearest neighbor (KNN), and random forest (RF). To confirm the effectiveness of the proposed RLDE method, experiments were conducted on two real hyperspectral datasets (Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Reflective Optics System Imaging Spectrometer (ROSIS)), and the proposed RLDE tri-training algorithm was compared with its counterparts of tri-training alone, LDE, and LFDA. The experiments confirmed that the proposed approach can effectively improve the classification accuracy for hyperspectral imagery. Full article
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Open AccessArticle
Improving Remote Sensing Image Super-Resolution Mapping Based on the Spatial Attraction Model by Utilizing the Pansharpening Technique
Remote Sens. 2019, 11(3), 247; https://doi.org/10.3390/rs11030247 - 26 Jan 2019
Cited by 1
Abstract
The spatial distribution information of remote sensing images can be derived by the super-resolution mapping (SRM) technique. Super-resolution mapping, based on the spatial attraction model (SRMSAM), has been an important SRM method, due to its simplicity and explicit physical meanings. However, the resolution [...] Read more.
The spatial distribution information of remote sensing images can be derived by the super-resolution mapping (SRM) technique. Super-resolution mapping, based on the spatial attraction model (SRMSAM), has been an important SRM method, due to its simplicity and explicit physical meanings. However, the resolution of the original remote sensing image is coarse, and the existing SRMSAM cannot take full advantage of the spatial–spectral information from the original image. To utilize more spatial–spectral information, improving remote sensing image super-resolution mapping based on the spatial attraction model by utilizing the pansharpening technique (SRMSAM-PAN) is proposed. In SRMSAM-PAN, a novel processing path, named the pansharpening path, is added to the existing SRMSAM. The original coarse remote sensing image is first fused with the high-resolution panchromatic image from the same area by the pansharpening technique in the novel pansharpening path, and the improved image is unmixed to obtain the novel fine-fraction images. The novel fine-fraction images from the pansharpening path and the existing fine-fraction images from the existing path are then integrated to produce finer-fraction images with more spatial–spectral information. Finally, the values predicted from the finer-fraction images are utilized to allocate class labels to all subpixels, to achieve the final mapping result. Experimental results show that the proposed SRMSAM-PAN can obtain a higher mapping accuracy than the existing SRMSAM methods. Full article
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Open AccessArticle
Hyperspectral Image Super-Resolution Inspired by Deep Laplacian Pyramid Network
by Zhi He and Lin Liu
Remote Sens. 2018, 10(12), 1939; https://doi.org/10.3390/rs10121939 - 02 Dec 2018
Cited by 5
Abstract
Existing hyperspectral sensors usually produce high-spectral-resolution but low-spatial-resolution images, and super-resolution has yielded impressive results in improving the resolution of the hyperspectral images (HSIs). However, most of the super-resolution methods require multiple observations of the same scene and improve the spatial resolution without [...] Read more.
Existing hyperspectral sensors usually produce high-spectral-resolution but low-spatial-resolution images, and super-resolution has yielded impressive results in improving the resolution of the hyperspectral images (HSIs). However, most of the super-resolution methods require multiple observations of the same scene and improve the spatial resolution without fully considering the spectral information. In this paper, we propose an HSI super-resolution method inspired by the deep Laplacian pyramid network (LPN). First, the spatial resolution is enhanced by an LPN, which can exploit the knowledge from natural images without using any auxiliary observations. The LPN progressively reconstructs the high-spatial-resolution images in a coarse-to-fine fashion by using multiple pyramid levels. Second, spectral characteristics between the low- and high-resolution HSIs are studied by the non-negative dictionary learning (NDL), which is proposed to learn the common dictionary with non-negative constraints. The super-resolution results can finally be obtained by multiplying the learned dictionary and its corresponding sparse codes. Experimental results on three hyperspectral datasets demonstrate the feasibility of the proposed method in enhancing the spatial resolution of the HSI with preserving the spectral information simultaneously. Full article
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Open AccessArticle
Spectral and Spatial Classification of Hyperspectral Images Based on Random Multi-Graphs
Remote Sens. 2018, 10(8), 1271; https://doi.org/10.3390/rs10081271 - 12 Aug 2018
Cited by 5
Abstract
Hyperspectral image classification has been acknowledged as the fundamental and challenging task of hyperspectral data processing. The abundance of spectral and spatial information has provided great opportunities to effectively characterize and identify ground materials. In this paper, we propose a spectral and spatial [...] Read more.
Hyperspectral image classification has been acknowledged as the fundamental and challenging task of hyperspectral data processing. The abundance of spectral and spatial information has provided great opportunities to effectively characterize and identify ground materials. In this paper, we propose a spectral and spatial classification framework for hyperspectral images based on Random Multi-Graphs (RMGs). The RMG is a graph-based ensemble learning method, which is rarely considered in hyperspectral image classification. It is empirically verified that the semi-supervised RMG deals well with small sample setting problems. This kind of problem is very common in hyperspectral image applications. In the proposed method, spatial features are extracted based on linear prediction error analysis and local binary patterns; spatial features and spectral features are then stacked into high dimensional vectors. The high dimensional vectors are fed into the RMG for classification. By randomly selecting a subset of features to create a graph, the proposed method can achieve excellent classification performance. The experiments on three real hyperspectral datasets have demonstrated that the proposed method exhibits better performance than several closely related methods. Full article
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Open AccessArticle
A New Low-Rank Representation Based Hyperspectral Image Denoising Method for Mineral Mapping
Remote Sens. 2017, 9(11), 1145; https://doi.org/10.3390/rs9111145 - 08 Nov 2017
Cited by 7
Abstract
Hyperspectral imaging technology has been used for geological analysis for many years wherein mineral mapping is the dominant application for hyperspectral images (HSIs). The very high spectral resolution of HSIs enables the identification and the diagnosis of different minerals with detection accuracy far [...] Read more.
Hyperspectral imaging technology has been used for geological analysis for many years wherein mineral mapping is the dominant application for hyperspectral images (HSIs). The very high spectral resolution of HSIs enables the identification and the diagnosis of different minerals with detection accuracy far beyond that offered by multispectral images. However, HSIs are inevitably corrupted by noise during acquisition and transmission processes. The presence of noise may significantly degrade the quality of the extracted mineral information. In order to improve the accuracy of mineral mapping, denoising is a crucial pre-processing task. By leveraging on low-rank and self-similarity properties of HSIs, this paper proposes a state-of-the-art HSI denoising algorithm that implements two main steps: (1) signal subspace learning via fine-tuned Robust Principle Component Analysis (RPCA); and (2) denoising the images associated with the representation coefficients, with respect to an orthogonal subspace basis, using BM3D, a self-similarity based state-of-the-art denoising algorithm. Accordingly, the proposed algorithm is named Hyperspectral Denoising via Robust principle component analysis and Self-similarity (HyDRoS), which can be considered as a supervised version of FastHyDe. The effectiveness of HyDRoS is evaluated in a series of mineral mapping experiments using noise-reduced AVIRIS and Hyperion HSIs. In these experiments, the proposed denoiser yielded systematically state-of-the-art performance. Full article
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Open AccessEditor’s ChoiceArticle
Retrieval of Biophysical Crop Variables from Multi-Angular Canopy Spectroscopy
Remote Sens. 2017, 9(7), 726; https://doi.org/10.3390/rs9070726 - 14 Jul 2017
Cited by 14
Abstract
The future German Environmental Mapping and Analysis Program (EnMAP) mission, due to launch in late 2019, will deliver high resolution hyperspectral data from space and will thus contribute to a better monitoring of the dynamic surface of the earth. Exploiting the satellite’s ±30° [...] Read more.
The future German Environmental Mapping and Analysis Program (EnMAP) mission, due to launch in late 2019, will deliver high resolution hyperspectral data from space and will thus contribute to a better monitoring of the dynamic surface of the earth. Exploiting the satellite’s ±30° across-track pointing capabilities will allow for the collection of hyperspectral time-series of homogeneous quality. Various studies have shown the possibility to retrieve geo-biophysical plant variables, like leaf area index (LAI) or leaf chlorophyll content (LCC), from narrowband observations with fixed viewing geometry by inversion of radiative transfer models (RTM). In this study we assess the capability of the well-known PROSPECT 5B + 4SAIL (Scattering by Arbitrarily Inclined Leaves) RTM to estimate these variables from off-nadir observations obtained during a field campaign with respect to EnMAP-like sun–target–sensor-geometries. A novel approach for multiple inquiries of a large look-up-table (LUT) in hierarchical steps is introduced that accounts for the varying instances of all variables of interest. Results show that anisotropic effects are strongest for early growth stages of the winter wheat canopy which influences also the retrieval of the variables. RTM inversions from off-nadir spectra lead to a decreased accuracy for the retrieval of LAI with a relative root mean squared error (rRMSE) of 18% at nadir vs. 25% (backscatter) and 24% (forward scatter) at off-nadir. For LCC estimations, however, off-nadir observations yield improvements, i.e., rRMSE (nadir) = 24% vs. rRMSE (forward scatter) = 20%. It follows that for a variable retrieval through RTM inversion, the final user will benefit from EnMAP time-series for biophysical studies regardless of the acquisition angle and will thus be able to exploit the maximum revisit capability of the mission. Full article
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