E-Mail Alert

Add your e-mail address to receive forthcoming issues of this journal:

Journal Browser

Journal Browser

Special Issue "Spatial Enhancement of Hyperspectral Data and Applications"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (20 May 2017)

Special Issue Editors

Guest Editor
Dr. Jonathan Cheung-Wai Chan

Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium
Website | E-Mail
Phone: +32 26291280
Interests: detailed mapping with hyperspectral images, superresolution image reconstruction, machine learning classification algorithms
Guest Editor
Prof. Yongqiang Zhao

School of Automation, Northwestern Polytechnical University, Youyi West Road 127#, Xi’An 710072, China
Website | E-Mail
Phone: +86 13384907328
Interests: hyperspectral remote sensing, superresolution, polarization imaging, image processing, sparse coding, image fusion, deep learning
Guest Editor
Dr. Naoto Yokoya

Department of Advanced Interdisciplinary Studies (AIS), University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo 153-8904, Japan
Website | E-Mail
Phone: +49 81 5328 4286
Interests: image processing, pattern recognition, data fusion, resolution enhancement, spectral unmixing, image restoration

Special Issue Information

Dear Colleagues,

Hyperspectral images with hundreds of contiguous spectral bands provide rich signal information. For many particular applications, such as detailed mapping, object identification, and mineral exploration, hyperspectral sensing is the only choice of data as conventional multi-spectral approach has been proven ineffective. Though airborne hyperspectral data can obtain very fine spatial resolution data, for large-scale investigations at regional to global scales, costs for acquisition and preprocessing of airborne hyperspectral images seem impractical. While future hyperspectral missions, such as EnMAP, the Japanese Hyperspectral Imager Suite (HISUI), the Italian PRISMA (Hyperspectral Precursor of the Application Mission), the US Hyperspectral Infrared Imager (HyspIRI), or the French HYPXIM will have global coverage, their spatial resolution remains too coarse for suitable use. As of a decade ago, there has been a surge of research on spatial enhancement of spaceborne hyperspectral data through superresolution reconstruction and image fusion techniques, for the latter the PANsharpening approach is most representative. Advancement of spatial enhancement methodology certainly provides a motivation for new applications, and a more urgent task is how to evaluate such resolution-enhanced data product. As such, the goal of this Special Issue is to gather experts active in the field to share most novel spatial enhancement approaches for hyperspectral data, as well as ways of validation. It is also important to disseminate such methods in terms of efficiency, consistency, and their effectiveness with concurrent satellite missions, for instance, EnMAP and Sentinel. As developments of various enhancement methods are maturing, it is of particular interest in their possible use in specific application and to show how the new strategy can advance scientific capability of hyperspectral remote sensing beyond conventional configuration constraints. Therefore, we would like to invite submission for the following topics:

  • Superresolution enhancement of HS image
  • Image fusion for spatial enhancement of HS image
  • Evaluation methodology for spatially enhanced HS image
  • Assessment of enhanced HS image for generic land cover/land use classification
  • Assessment of enhanced HS image for conventional and innovative applications

Authors are required to check and follow specific Instructions to Authors, see https://dl.dropboxusercontent.com/u/165068305/Remote_Sensing-Additional_Instructions.pdf.

Dr. Jonathan Cheung-Wai Chan
Prof. Yongqiang Zhao
Dr. Naoto Yokoya
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 monthly 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 1600 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.

Published Papers (6 papers)

View options order results:
result details:
Displaying articles 1-6
Export citation of selected articles as:

Research

Open AccessArticle Spatial Resolution Enhancement of Hyperspectral Images Using Spectral Unmixing and Bayesian Sparse Representation
Remote Sens. 2017, 9(6), 541; doi:10.3390/rs9060541
Received: 9 February 2017 / Revised: 17 May 2017 / Accepted: 23 May 2017 / Published: 31 May 2017
Cited by 1 | PDF Full-text (1646 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, a new method is presented for spatial resolution enhancement of hyperspectral images (HSI) using spectral unmixing and a Bayesian sparse representation. The proposed method combines the high spectral resolution from the HSI with the high spatial resolution from a multispectral
[...] Read more.
In this paper, a new method is presented for spatial resolution enhancement of hyperspectral images (HSI) using spectral unmixing and a Bayesian sparse representation. The proposed method combines the high spectral resolution from the HSI with the high spatial resolution from a multispectral image (MSI) of the same scene and high resolution images from unrelated scenes. The fusion method is based on a spectral unmixing procedure for which the endmember matrix and the abundance fractions are estimated from the HSI and MSI, respectively. A Bayesian formulation of this method leads to an ill-posed fusion problem. A sparse representation regularization term is added to convert it into a well-posed inverse problem. In the sparse representation, dictionaries are constructed from the MSI, high optical resolution images, synthetic aperture radar (SAR) or combinations of them. The proposed algorithm is applied to real datasets and compared with state-of-the-art fusion algorithms based on spectral unmixing and sparse representation, respectively. The proposed method significantly increases the spatial resolution and decreases the spectral distortion efficiently. Full article
(This article belongs to the Special Issue Spatial Enhancement of Hyperspectral Data and Applications)
Figures

Open AccessArticle Texture-Guided Multisensor Superresolution for Remotely Sensed Images
Remote Sens. 2017, 9(4), 316; doi:10.3390/rs9040316
Received: 4 January 2017 / Revised: 14 March 2017 / Accepted: 24 March 2017 / Published: 28 March 2017
PDF Full-text (6236 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a novel technique, namely texture-guided multisensor superresolution (TGMS), for fusing a pair of multisensor multiresolution images to enhance the spatial resolution of a lower-resolution data source. TGMS is based on multiresolution analysis, taking object structures and image textures in the
[...] Read more.
This paper presents a novel technique, namely texture-guided multisensor superresolution (TGMS), for fusing a pair of multisensor multiresolution images to enhance the spatial resolution of a lower-resolution data source. TGMS is based on multiresolution analysis, taking object structures and image textures in the higher-resolution image into consideration. TGMS is designed to be robust against misregistration and the resolution ratio and applicable to a wide variety of multisensor superresolution problems in remote sensing. The proposed methodology is applied to six different types of multisensor superresolution, which fuse the following image pairs: multispectral and panchromatic images, hyperspectral and panchromatic images, hyperspectral and multispectral images, optical and synthetic aperture radar images, thermal-hyperspectral and RGB images, and digital elevation model and multispectral images. The experimental results demonstrate the effectiveness and high general versatility of TGMS. Full article
(This article belongs to the Special Issue Spatial Enhancement of Hyperspectral Data and Applications)
Figures

Open AccessFeature PaperArticle No-Reference Hyperspectral Image Quality Assessment via Quality-Sensitive Features Learning
Remote Sens. 2017, 9(4), 305; doi:10.3390/rs9040305
Received: 17 January 2017 / Revised: 13 March 2017 / Accepted: 20 March 2017 / Published: 23 March 2017
PDF Full-text (22305 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Assessing the quality of a reconstructed hyperspectral image (HSI) is of significance for restoration and super-resolution. Current image quality assessment methods such as peak signal-noise-ratio require the availability of pristine reference image, which is often not available in reality. In this paper, we
[...] Read more.
Assessing the quality of a reconstructed hyperspectral image (HSI) is of significance for restoration and super-resolution. Current image quality assessment methods such as peak signal-noise-ratio require the availability of pristine reference image, which is often not available in reality. In this paper, we propose a no-reference hyperspectral image quality assessment method based on quality-sensitive features extraction. Difference of statistical properties between pristine and distorted HSIs is analyzed in both spectral and spatial domains, then multiple statistics features that are sensitive to image quality are extracted. By combining all these statistics features, we learn a multivariate Gaussian (MVG) model as benchmark from the pristine hyperspectral datasets. In order to assess the quality of a reconstructed HSI, we partition it into different local blocks and fit a MVG model on each block. A modified Bhattacharyya distance between the MVG model of each reconstructed HSI block and the benchmark MVG model is computed to measure the quality. The final quality score is obtained by average pooling over all the blocks. We assess five state-of-the-art super-resolution methods on Airborne Visible Infrared Imaging Spectrometer (AVIRIS) and Hyperspec-VNIR-C (HyperspecVC) data using our proposed method. It is verified that the proposed quality score is consistent with current reference-based assessment indices, which demonstrates the effectiveness and potential of the proposed no-reference image quality assessment method. Full article
(This article belongs to the Special Issue Spatial Enhancement of Hyperspectral Data and Applications)
Figures

Figure 1

Open AccessArticle Image Fusion for Spatial Enhancement of Hyperspectral Image via Pixel Group Based Non-Local Sparse Representation
Remote Sens. 2017, 9(1), 53; doi:10.3390/rs9010053
Received: 2 September 2016 / Revised: 28 December 2016 / Accepted: 3 January 2017 / Published: 9 January 2017
PDF Full-text (4917 KB) | HTML Full-text | XML Full-text
Abstract
Restricted by technical and budget constraints, hyperspectral images (HSIs) are usually obtained with low spatial resolution. In order to improve the spatial resolution of a given hyperspectral image, a new spatial and spectral image fusion approach via pixel group based non-local sparse representation
[...] Read more.
Restricted by technical and budget constraints, hyperspectral images (HSIs) are usually obtained with low spatial resolution. In order to improve the spatial resolution of a given hyperspectral image, a new spatial and spectral image fusion approach via pixel group based non-local sparse representation is proposed, which exploits the spectral sparsity and spectral non-local self-similarity of the hyperspectral image. The proposed approach fuses the hyperspectral image with a high-spatial-resolution multispectral image of the same scene to obtain a hyperspectral image with high spatial and spectral resolutions. The input hyperspectral image is used to train the spectral dictionary, while the sparse codes of the desired HSI are estimated by jointly encoding the similar pixels in each pixel group extracted from the high-spatial-resolution multispectral image. To improve the accuracy of the pixel group based non-local sparse representation, the similar pixels in a pixel group are selected by utilizing both the spectral and spatial information. The performance of the proposed approach is tested on two remote sensing image datasets. Experimental results suggest that the proposed method outperforms a number of sparse representation based fusion techniques, and can preserve the spectral information while recovering the spatial details under large magnification factors. Full article
(This article belongs to the Special Issue Spatial Enhancement of Hyperspectral Data and Applications)
Figures

Open AccessArticle Joint Sparse Sub-Pixel Mapping Model with Endmember Variability for Remotely Sensed Imagery
Remote Sens. 2017, 9(1), 15; doi:10.3390/rs9010015
Received: 5 September 2016 / Revised: 17 December 2016 / Accepted: 21 December 2016 / Published: 29 December 2016
Cited by 1 | PDF Full-text (3085 KB) | HTML Full-text | XML Full-text
Abstract
Spectral unmixing and sub-pixel mapping have been used to estimate the proportion and spatial distribution of the different land-cover classes in mixed pixels at a sub-pixel scale. In the past decades, several algorithms were proposed in both categories; however, these two techniques are
[...] Read more.
Spectral unmixing and sub-pixel mapping have been used to estimate the proportion and spatial distribution of the different land-cover classes in mixed pixels at a sub-pixel scale. In the past decades, several algorithms were proposed in both categories; however, these two techniques are generally regarded as independent procedures, with most sub-pixel mapping methods using abundance maps generated by spectral unmixing techniques. It should be noted that the utilized abundance map has a strong impact on the performance of the subsequent sub-pixel mapping process. Recently, we built a novel sub-pixel mapping model in combination with the linear spectral mixture model. Therefore, a joint sub-pixel mapping model was established that connects an original (coarser resolution) remotely sensed image with the final sub-pixel result directly. However, this approach focuses on incorporating the spectral information contained in the original image without addressing the spectral endmember variability resulting from variable illumination and environmental conditions. To address this important issue, in this paper we designed a new joint sparse sub-pixel mapping method under the assumption that various representative spectra for each endmember are known a priori and available in a library. In addition, the total variation (TV) regularization was also adopted to exploit the spatial information. The proposed approach was experimentally evaluated using both synthetic and real hyperspectral images, and the obtained results demonstrate that the method can achieve better results by considering the impact of endmember variability when compared with other sub-pixel mapping methods. Full article
(This article belongs to the Special Issue Spatial Enhancement of Hyperspectral Data and Applications)
Figures

Open AccessArticle Nonlocal Total Variation Subpixel Mapping for Hyperspectral Remote Sensing Imagery
Remote Sens. 2016, 8(3), 250; doi:10.3390/rs8030250
Received: 12 December 2015 / Revised: 3 March 2016 / Accepted: 11 March 2016 / Published: 16 March 2016
Cited by 5 | PDF Full-text (7202 KB) | HTML Full-text | XML Full-text
Abstract
Subpixel mapping is a method of enhancing the spatial resolution of images, which involves dividing a mixed pixel into subpixels and assigning each subpixel to a definite land-cover class. Traditionally, subpixel mapping is based on the assumption of spatial dependence, and the spatial
[...] Read more.
Subpixel mapping is a method of enhancing the spatial resolution of images, which involves dividing a mixed pixel into subpixels and assigning each subpixel to a definite land-cover class. Traditionally, subpixel mapping is based on the assumption of spatial dependence, and the spatial correlation information among pixels and subpixels is considered in the prediction of the spatial locations of land-cover classes within the mixed pixels. In this paper, a novel subpixel mapping method for hyperspectral remote sensing imagery based on a nonlocal method, namely nonlocal total variation subpixel mapping (NLTVSM), is proposed to use the nonlocal self-similarity prior to improve the performance of the subpixel mapping task. Differing from the existing spatial regularization subpixel mapping technique, in NLTVSM, the nonlocal total variation is used as a spatial regularizer to exploit the similar patterns and structures in the image. In this way, the proposed method can obtain an optimal subpixel mapping result and accuracy by considering the nonlocal spatial information. Compared with the classical and state-of-the-art subpixel mapping approaches, the experimental results using a simulated hyperspectral image, two synthetic hyperspectral remote sensing images, and a real hyperspectral image confirm that the proposed algorithm can obtain better results in both visual and quantitative evaluations. Full article
(This article belongs to the Special Issue Spatial Enhancement of Hyperspectral Data and Applications)
Figures

Journal Contact

MDPI AG
Remote Sensing Editorial Office
St. Alban-Anlage 66, 4052 Basel, Switzerland
E-Mail: 
Tel. +41 61 683 77 34
Fax: +41 61 302 89 18
Editorial Board
Contact Details Submit to Remote Sensing Edit a special issue Review for Remote Sensing
logo
loading...
Back to Top