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Spatial Enhancement of Hyperspectral Data and Applications

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

Deadline for manuscript submissions: closed (20 May 2017) | Viewed by 82923

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Prof. Dr. Jonathan C-W Chan

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Department of Electronics and Informatics, Vrije Universiteit Brussel, 1050 Brussels, Belgium
Interests: hyperspectral analysis; land cover classification; machine learning; superresolution enhancement
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Prof. Yongqiang Zhao

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Guest Editor

School of Automation, Northwestern Polytechnical University, Youyi West Road 127#, Xi’An 710072, China
Interests: hyperspectral remote sensing; superresolution; polarization imaging; image processing; sparse coding; image fusion; deep learning
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Dr. Naoto Yokoya

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Guest Editor

RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, Japan
Interests: remote sensing; image processing; data fusion; machine learning; disaster management; environmental monitoring
Special Issues, Collections and Topics in MDPI journals

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

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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.

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Published Papers (9 papers)

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Research

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2475 KiB

Open AccessArticle

Hyperspectral Image Super-Resolution via Nonlocal Low-Rank Tensor Approximation and Total Variation Regularization

by Yao Wang, Xi’ai Chen, Zhi Han and Shiying He

Remote Sens. 2017, 9(12), 1286; https://doi.org/10.3390/rs9121286 - 11 Dec 2017

Cited by 79 | Viewed by 6403

Abstract

Hyperspectral image (HSI) possesses three intrinsic characteristics: the global correlation across spectral domain, the nonlocal self-similarity across spatial domain, and the local smooth structure across both spatial and spectral domains. This paper proposes a novel tensor based approach to handle the problem of HSI spatial super-resolution by modeling such three underlying characteristics. Specifically, a noncovex tensor penalty is used to exploit the former two intrinsic characteristics hidden in several 4D tensors formed by nonlocal similar patches within the 3D HSI. In addition, the local smoothness in both spatial and spectral modes of the HSI cube is characterized by a 3D total variation (TV) term. Then, we develop an effective algorithm for solving the resulting optimization by using the local linear approximation (LLA) strategy and the alternative direction method of multipliers (ADMM). A series of experiments are carried out to illustrate the superiority of the proposed approach over some state-of-the-art approaches. Full article

(This article belongs to the Special Issue Spatial Enhancement of Hyperspectral Data and Applications)

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22314 KiB

Open AccessArticle

Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network

by Shaohui Mei, Xin Yuan, Jingyu Ji, Yifan Zhang, Shuai Wan and Qian Du

Remote Sens. 2017, 9(11), 1139; https://doi.org/10.3390/rs9111139 - 7 Nov 2017

Cited by 256 | Viewed by 16062

Abstract

Hyperspectral images are well-known for their fine spectral resolution to discriminate different materials. However, their spatial resolution is relatively low due to the trade-off in imaging sensor technologies, resulting in limitations in their applications. Inspired by recent achievements in convolutional neural network (CNN) based super-resolution (SR) for natural images, a novel three-dimensional full CNN (3D-FCNN) is constructed for spatial SR of hyperspectral images in this paper. Specifically, 3D convolution is used to exploit both the spatial context of neighboring pixels and spectral correlation of neighboring bands, such that spectral distortion when directly applying traditional CNN based SR algorithms to hyperspectral images in band-wise manners is alleviated. Furthermore, a sensor-specific mode is designed for the proposed 3D-FCNN such that none of the samples from the target scene are required for training. Fine-tuning by a small number of training samples from the target scene can further improve the performance of such a sensor-specific method. Extensive experimental results on four benchmark datasets from two well-known hyperspectral sensors, namely hyperspectral digital imagery collection experiment (HYDICE) and reflective optics system imaging spectrometer (ROSIS) sensors, demonstrate that our proposed 3D-FCNN outperforms several existing SR methods by ensuring higher quality both in reconstruction and spectral fidelity. Full article

(This article belongs to the Special Issue Spatial Enhancement of Hyperspectral Data and Applications)

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1646 KiB

Open AccessArticle

Spatial Resolution Enhancement of Hyperspectral Images Using Spectral Unmixing and Bayesian Sparse Representation

by Elham Kordi Ghasrodashti, Azam Karami, Rob Heylen and Paul Scheunders

Remote Sens. 2017, 9(6), 541; https://doi.org/10.3390/rs9060541 - 31 May 2017

Cited by 31 | Viewed by 6097

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 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)

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6236 KiB

Open AccessArticle

Texture-Guided Multisensor Superresolution for Remotely Sensed Images

by Naoto Yokoya

Remote Sens. 2017, 9(4), 316; https://doi.org/10.3390/rs9040316 - 28 Mar 2017

Cited by 11 | Viewed by 5632

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 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)

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22305 KiB

Open AccessFeature PaperArticle

No-Reference Hyperspectral Image Quality Assessment via Quality-Sensitive Features Learning

by Jingxiang Yang, Yongqiang Zhao, Chen Yi and Jonathan Cheung-Wai Chan

Remote Sens. 2017, 9(4), 305; https://doi.org/10.3390/rs9040305 - 23 Mar 2017

Cited by 91 | Viewed by 8325

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 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)

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4917 KiB

Open AccessArticle

Image Fusion for Spatial Enhancement of Hyperspectral Image via Pixel Group Based Non-Local Sparse Representation

by Jing Yang, Ying Li, Jonathan Cheung-Wai Chan and Qiang Shen

Remote Sens. 2017, 9(1), 53; https://doi.org/10.3390/rs9010053 - 9 Jan 2017

Cited by 25 | Viewed by 7412

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 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)

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3085 KiB

Open AccessArticle

Joint Sparse Sub-Pixel Mapping Model with Endmember Variability for Remotely Sensed Imagery

by Xiong Xu, Xiaohua Tong, Antonio Plaza, Yanfei Zhong, Huan Xie and Liangpei Zhang

Remote Sens. 2017, 9(1), 15; https://doi.org/10.3390/rs9010015 - 29 Dec 2016

Cited by 20 | Viewed by 7136

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 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)

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7202 KiB

Open AccessArticle

Nonlocal Total Variation Subpixel Mapping for Hyperspectral Remote Sensing Imagery

by Ruyi Feng, Yanfei Zhong, Yunyun Wu, Da He, Xiong Xu and Liangpei Zhang

Remote Sens. 2016, 8(3), 250; https://doi.org/10.3390/rs8030250 - 16 Mar 2016

Cited by 22 | Viewed by 6989

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 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)

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Review

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32 pages, 500 KiB

Open AccessEditor’s ChoiceReview

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context

by Julie Transon, Raphaël D’Andrimont, Alexandre Maugnard and Pierre Defourny

Remote Sens. 2018, 10(2), 157; https://doi.org/10.3390/rs10020157 - 23 Jan 2018

Cited by 220 | Viewed by 17118

Abstract

In the last few decades, researchers have developed a plethora of hyperspectral Earth Observation (EO) remote sensing techniques, analysis and applications. While hyperspectral exploratory sensors are demonstrating their potential, Sentinel-2 multispectral satellite remote sensing is now providing free, open, global and systematic high resolution visible and infrared imagery at a short revisit time. Its recent launch suggests potential synergies between multi- and hyper-spectral data. This study, therefore, reviews 20 years of research and applications in satellite hyperspectral remote sensing through the analysis of Earth observation hyperspectral sensors’ publications that cover the Sentinel-2 spectrum range: Hyperion, TianGong-1, PRISMA, HISUI, EnMAP, Shalom, HyspIRI and HypXIM. More specifically, this study (i) brings face to face past and future hyperspectral sensors’ applications with Sentinel-2’s and (ii) analyzes the applications’ requirements in terms of spatial and temporal resolutions. Eight main application topics were analyzed including vegetation, agriculture, soil, geology, urban, land use, water resources and disaster. Medium spatial resolution, long revisit time and low signal-to-noise ratio in the short-wave infrared of some hyperspectral sensors were highlighted as major limitations for some applications compared to the Sentinel-2 system. However, these constraints mainly concerned past hyperspectral sensors, while they will probably be overcome by forthcoming instruments. Therefore, this study is putting forward the compatibility of hyperspectral sensors and Sentinel-2 systems for resolution enhancement techniques in order to increase the panel of hyperspectral uses. Full article

(This article belongs to the Special Issue Spatial Enhancement of Hyperspectral Data and Applications)

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