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Book ; Online: Hyperspectral Imaging and Applications

Chang, Chein-I / Song, Meiping / Zhang, Junping / Wu, Chao-Cheng

2022

Keywords	Technology: general issues ; History of engineering & technology ; biodiversity ; peatland ; vegetation type ; classification ; hyperspectral ; in situ measurements ; hyperspectral image (HSI) ; multiscale union regions adaptive sparse representation (MURASR) ; multiscale spatial information ; imaging spectroscopy ; airborne laser scanning ; minimum noise fraction ; class imbalance ; Africa ; agroforestry ; tree species ; hyperspectral unmixing ; endmember extraction ; band selection ; spectral variability ; prototype space ; ensemble learning ; rotation forest ; semi-supervised local discriminant analysis ; optical spectral region ; thermal infrared spectral region ; mineral mapping ; data integration ; HyMap ; AHS ; raw material ; remote sensing ; nonnegative matrix factorization ; data-guided constraints ; sparseness ; evenness ; hashing ensemble ; hierarchical feature ; hyperspectral classification ; band expansion process (BEP) ; constrained energy minimization (CEM) ; correlation band expansion process (CBEP) ; iterative CEM (ICEM) ; nonlinear band expansion (NBE) ; Otsu's method ; sparse unmixing ; local abundance ; nuclear norm ; hyperspectral detection ; target detection ; sprout detection ; constrained energy minimization ; iterative algorithm ; adaptive window ; hyperspectral imagery ; recursive anomaly detection ; local summation RX detector (LS-RXD) ; sliding window ; band selection (BS) ; band subset selection (BSS) ; hyperspectral image classification ; linearly constrained minimum variance (LCMV) ; successive LCMV-BSS (SC LCMV-BSS) ; sequential LCMV-BSS (SQ LCMV-BSS) ; vicarious calibration ; reflectance-based method ; irradiance-based method ; Dunhuang site ; 90° yaw imaging ; terrestrial hyperspectral imaging ; vineyard ; water stress ; machine learning ; tree-based ensemble ; progressive sample processing (PSP) ; real-time processing ; image fusion ; hyperspectral image ; panchromatic image ; structure tensor ; image enhancement ; weighted fusion ; spectral mixture analysis ; fire severity ; AVIRIS ; deep belief networks ; deep learning ; texture feature enhancement ; band grouping ; hyperspectral compression ; lossy compression ; on-board compression ; orthogonal projections ; Gram-Schmidt orthogonalization ; parallel processing ; anomaly detection ; sparse coding ; KSVD ; hyperspectral images (HSIs) ; SVM ; composite kernel ; algebraic multigrid methods ; hyperspectral pansharpening ; panchromatic ; intrinsic image decomposition ; weighted least squares filter ; spectral-spatial classification ; label propagation ; superpixel ; semi-supervised learning ; rolling guidance filtering (RGF) ; graph ; deep pipelined background statistics ; high-level synthesis ; data fusion ; data unmixing ; hyperspectral imaging
Language	0\|e
Size	1 electronic resource (632 pages)
Publisher	MDPI - Multidisciplinary Digital Publishing Institute
Publishing place	Basel
Document type	Book ; Online
Note	English ; Open Access
HBZ-ID	HT021613101
ISBN	9783039215232 ; 303921523X
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Book ; Online: Advances in Hyperspectral Data Exploitation

Chang, Chein-I / Song, Meiping / Yu, Chunyan / Wang, Yulei / Yu, Haoyang / Li, Jiaojiao / Wang, Lin / Li, Hsiao-Chi / Li, Xiaorun

2022

Keywords	Technology: general issues ; History of engineering & technology ; hyperspectral image few-shot classification ; deep learning ; meta-learning ; relation network ; convolutional neural network ; constrained-target optimal index factor band selection (CTOIFBS) ; hyperspectral image ; underwater spectral imaging system ; underwater hyperspectral target detection ; band selection (BS) ; constrained energy minimization (CEM) ; lightweight convolutional neural networks ; hyperspectral imagery classification ; transfer learning ; air temperature ; spatial measurement ; FTIR ; MWIR ; carbon dioxide absorption ; target detection ; coffee beans ; insect damage ; hyperspectral imaging ; band selection ; visualization ; color formation models ; multispectral image ; image fusion ; joint tensor decomposition ; anomaly detection ; constrained sparse representation ; hyperspectral imagery ; moving target detection ; spatio-temporal processing ; hyperspectral remote sensing ; image classification ; constraint representation ; superpixel segmentation ; multiscale decision fusion ; plug-and-play ; denoising ; nonlinear unmixing ; spectral reconstruction ; residual augmented attentional u-shape network ; spatial augmented attention ; channel augmented attention ; boundary-aware constraint ; atmospheric transmittance ; temperature ; emissivity ; separation ; midwave infrared ; hyperspectral images ; hyperspectral image super-resolution ; data fusion ; spectral-spatial residual network ; self-supervised training ; hyperspectral ; vegetation ; generative adversarial network ; data augmentation ; classification ; rice leaf blast ; hyperspectral imaging data ; deep convolutional neural networks ; fused features ; evolutionary computation ; heuristic algorithms ; machine learning ; unmanned aerial vehicles (UAVs) ; vegetation mapping ; upland swamps ; mine environment ; rice ; rice leaf folder ; hyperspectral image classification ; change detection ; self-supervised learning ; attention mechanism ; multi-source image fusion ; SFIM ; least square estimation ; spatial filter ; hyperspectral imaging (HSI) ; hyperspectral target detection ; hyperspectral reconstruction ; hyperspectral unmixing
Language	English
Size	1 electronic resource (434 pages)
Publisher	MDPI - Multidisciplinary Digital Publishing Institute
Publishing place	Basel
Document type	Book ; Online
Note	English
HBZ-ID	HT030375357
ISBN	9783036557960 ; 3036557962
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article ; Online: Standardization of near infrared spectroscopies via sample spectral correlation equalization.

Xue, Bai / Cloud, Glenn / Vishnyakov, Sergey / Mehta, Zubin / Ramer, Evan / Jin, Feng / Song, Meiping / Chang, Chein-I

Analytica chimica acta

2023 Volume 1252, Page(s) 341031

Abstract: A novel method for near-infrared (NIR) spectroscopy spectra standardization is presented. NIR spectroscopies have been widely used in analytical chemistry, and many methods have been developed for NIR spectra standardization. To establish a robust ... ...

Abstract	A novel method for near-infrared (NIR) spectroscopy spectra standardization is presented. NIR spectroscopies have been widely used in analytical chemistry, and many methods have been developed for NIR spectra standardization. To establish a robust standardization transformation, most existing methods require spectral data sets from both primal and secondary instruments for 1-1 correspondence validation. However, this limits the usage of standardization methods. This paper investigates an interesting issue, "Can spectra data in sets be arbitrarily order?" and further develops a completely different approach from existing methods in view of statistical signal processing. The key idea is to first compensate for the distortion along the wavelength and intensity of the spectra, and then transfer the second order statistic (2OS) from the primal spectra to the secondary spectra via data sphering and an inverse sphering transform so that the 2OS can be estimated regardless of the sample statistic order. To further demonstrate how the developed method can extend the usage of the NIR spectra standardization, several application-driven experiments on classification and regression are conducted for demonstration, and a comparison to the piecewise direct standardization (PDS) is also studied.
Language	English
Publishing date	2023-03-07
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	1483436-4
ISSN	1873-4324 ; 0003-2670
ISSN (online)	1873-4324
ISSN	0003-2670
DOI	10.1016/j.aca.2023.341031
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Underwater Hyperspectral Target Detection with Band Selection

Fu, Xianping / Shang, Xiaodi / Sun, Xudong / Yu, Haoyang / Song, Meiping / Chang, Chein-I

Remote Sensing. 2020 Mar. 25, v. 12, no. 7

2020

Abstract: Compared to multi-spectral imagery, hyperspectral imagery has very high spectral resolution with abundant spectral information. In underwater target detection, hyperspectral technology can be advantageous in the sense of a poor underwater imaging ... ...

Abstract	Compared to multi-spectral imagery, hyperspectral imagery has very high spectral resolution with abundant spectral information. In underwater target detection, hyperspectral technology can be advantageous in the sense of a poor underwater imaging environment, complex background, or protective mechanism of aquatic organisms. Due to high data redundancy, slow imaging speed, and long processing of hyperspectral imagery, a direct use of hyperspectral images in detecting targets cannot meet the needs of rapid detection of underwater targets. To resolve this issue, a fast, hyperspectral underwater target detection approach using band selection (BS) is proposed. It first develops a constrained-target optimal index factor (OIF) band selection (CTOIFBS) to select a band subset with spectral wavelengths specifically responding to the targets of interest. Then, an underwater spectral imaging system integrated with the best-selected band subset is constructed for underwater target image acquisition. Finally, a constrained energy minimization (CEM) target detection algorithm is used to detect the desired underwater targets. Experimental results demonstrate that the band subset selected by CTOIFBS is more effective in detecting underwater targets compared to the other three existing BS methods, uniform band selection (UBS), minimum variance band priority (MinV-BP), and minimum variance band priority with OIF (MinV-BP-OIF). In addition, the results also show that the acquisition and detection speed of the designed underwater spectral acquisition system using CTOIFBS can be significantly improved over the original underwater hyperspectral image system without BS.
Keywords	algorithms ; aquatic organisms ; energy ; hyperspectral imagery ; image analysis ; multispectral imagery ; rapid methods ; remote sensing ; variance ; wavelengths
Language	English
Dates of publication	2020-0325
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs12071056
Database	NAL-Catalogue (AGRICOLA)

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Article: Optical Remote Sensing Image Registration Using Spatial-Consistency and Average Regional Information Divergence Minimization via Quantum-Behaved Particle Swarm Optimization

Chen, Shuhan / Xue, Bai / Yang, Han / Li, Xiaorun / Zhao, Liaoying / Chang, Chein-I

Remote Sensing. 2020 Sept. 19, v. 12, no. 18

2020

Abstract: Due to invariance to significant intensity differences, similarity metrics have been widely used as criteria for an area-based method for registering optical remote sensing image. However, for images with large scale and rotation difference, the ... ...

Abstract	Due to invariance to significant intensity differences, similarity metrics have been widely used as criteria for an area-based method for registering optical remote sensing image. However, for images with large scale and rotation difference, the robustness of similarity metrics can greatly determine the registration accuracy. In addition, area-based methods usually require appropriately selected initial values for registration parameters. This paper presents a registration approach using spatial consistency (SC) and average regional information divergence (ARID), called spatial-consistency and average regional information divergence minimization via quantum-behaved particle swarm optimization (SC-ARID-QPSO) for optical remote sensing images registration. Its key idea minimizes ARID with SC to select an ARID-minimized spatial consistent feature point set. Then, the selected consistent feature set is tuned randomly to generate a set of M registration parameters, which provide initial particle warms to implement QPSO to obtain final optimal registration parameters. The proposed ARID is used as a criterion for the selection of consistent feature set, the generation of initial parameter sets, and fitness functions used by QPSO. The iterative process of QPSO is terminated based on a custom-designed automatic stopping rule. To evaluate the performance of SC-ARID-QPSO, both simulated and real images are used for experiments for validation. In addition, two data sets are particularly designed to conduct a comparative study and analysis with existing state-of-the-art methods. The experimental results demonstrate that SC-ARID-QPSO produces better registration accuracy and robustness than compared methods.
Keywords	accuracy ; algorithms ; comparative study ; data collection ; image analysis ; information ; paper ; remote sensing
Language	English
Dates of publication	2020-0919
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
Note	NAL-light
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs12183066
Database	NAL-Catalogue (AGRICOLA)

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Book: Hyperspectral data processing

Chang, Chein-I

algorithm design and analysis

2013

Abstract: This book is intended to be a sequel from the author's other title with Kluwer "Hyperspectral Imaging: Techniques for Spectral Detection and Classification". It contains five major parts. Part I is new aspects of OSP including 7 chapters, OSP revisit, ... ...

Author's details	Chein-I Chang
Abstract	"This book is intended to be a sequel from the author's other title with Kluwer "Hyperspectral Imaging: Techniques for Spectral Detection and Classification". It contains five major parts. Part I is new aspects of OSP including 7 chapters, OSP revisit, generalized OSP, FPGA designs for OSP and CEM, Kalman filter-based linear unmixing, least squares fully constrained linear mixture analysis, exploitation-based hyperspectral data compression and size estimation of supixel targets, Part II is interference rejection for linear unmixing composed of three chapters, signal-composed interference-annihilated theory, interference-annihilated noise-adjusted theory and information-processed matched filter theory; Part III is nonlinear non-literal techniques for linear unmixing consisting of 3 chapters, convex cone analysis, information theoretic criterion-based project pursuit and nonlinear mixing model analysis; Part IV is spectral coding comprising of three chapters, progressive spectral coding, spectral binary coding and spectral coding for band selection; Part V is applications made up of two chapters, applications to magnetic resonance imaging and landmine detection"--
Keywords	Image processing/Digital techniques ; Signal processing ; Spectroscopic imaging
Language	English
Size	XXVII, 1135 S., Ill., graph. Darst., Kt.
Publisher	Wiley
Publishing place	Hoboken, NJ
Document type	Book
Note	Literaturverz. S. 1052 - 1069
ISBN	0471690562 ; 9780471690566
Database	Library catalogue of the German National Library of Science and Technology (TIB), Hannover

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Article: Editorial for Special Issue “Hyperspectral Imaging and Applications”

Chang, Chein-I / Song, Meiping / Zhang, Junping / Wu, Chao-Cheng

Remote Sensing. 2019 Aug. 27, v. 11, no. 17

2019

Abstract: Due to advent of sensor technology, hyperspectral imaging has become an emerging technology in remote sensing. Many problems, which cannot be resolved by multispectral imaging, can now be solved by hyperspectral imaging. The aim of this Special Issue “ ... ...

Abstract	Due to advent of sensor technology, hyperspectral imaging has become an emerging technology in remote sensing. Many problems, which cannot be resolved by multispectral imaging, can now be solved by hyperspectral imaging. The aim of this Special Issue “Hyperspectral Imaging and Applications” is to publish new ideas and technologies to facilitate the utility of hyperspectral imaging in data exploitation and to further explore its potential in different applications. This Special Issue has accepted and published 25 papers in various areas, which can be organized into 7 categories, Data Unmixing, Spectral variability, Target Detection, Hyperspectral Image Classification, Band Selection, Data Fusion, Applications.
Keywords	hyperspectral imagery ; image analysis ; multispectral imagery ; remote sensing
Language	English
Dates of publication	2019-0827
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs11172012
Database	NAL-Catalogue (AGRICOLA)

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Article: Fusion of Various Band Selection Methods for Hyperspectral Imagery

Wang, Yulei / Wang, Lin / Xie, Hongye / Chang, Chein-I

Remote Sensing. 2019 Sept. 12, v. 11, no. 18

2019

Abstract: This paper presents an approach to band selection fusion (BSF) which fuses bands produced by a set of different band selection (BS) methods for a given number of bands to be selected, n ... BS ... Since each BS method has its own merit in finding the ... ...

Abstract	This paper presents an approach to band selection fusion (BSF) which fuses bands produced by a set of different band selection (BS) methods for a given number of bands to be selected, n<inf>BS</inf>. Since each BS method has its own merit in finding the desired bands, various BS methods produce different band subsets with the same n<inf>BS</inf>. In order to take advantage of these different band subsets, the proposed BSF is performed by first finding the union of all band subsets produced by a set of BS methods as a joint band subset (JBS). Due to the fact that a band selected by one BS method in JBS may be also selected by other BS methods, in this case each band in JBS is prioritized by the frequency of the band appearing in the band subsets to be fused. Such frequency is then used to calculate the priority probability of this particular band in the JBS. Because the JBS is obtained by taking the union of all band subsets, the number of bands in the JBS is at least equal to or greater than n<inf>BS</inf>. So, there may be more than n<inf>BS</inf> bands, in which case, BSF uses the frequency-calculated priority probabilities to select n<inf>BS</inf> bands from JBS. Two versions of BSF, called progressive BSF and simultaneous BSF, are developed for this purpose. Of particular interest is that BSF can prioritize bands without band de-correlation, which has been a major issue in many BS methods using band prioritization as a criterion to select bands.
Keywords	hyperspectral imagery ; prioritization ; probability ; remote sensing ; selection methods
Language	English
Dates of publication	2019-0912
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs11182125
Database	NAL-Catalogue (AGRICOLA)

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Book: Hyperspectral data exploitation

Chang, Chein-I

theory and applications

2007

Author's details	edited by Chein-I Chang
Keywords	Remote sensing. ; Multispectral photography. ; Image processing/Digital techniques.
Language	English
Size	x, 430 p. :, ill. ;, 25 cm.
Publisher	Wiley-Interscience
Publishing place	Hoboken, N.J
Document type	Book
ISBN	9780471746973 ; 0471746975
Database	NAL-Catalogue (AGRICOLA)

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Book: Hyperspectral data exploitation

Chang, Chein-I

theory and applications

2007

Author's details	ed. by Chein-I. Chang
Keywords	Image processing/Digital techniques ; Multispectral photography ; Remote sensing
Language	English
Size	X, 430 S., Ill., graph. Darst.
Publisher	Wiley
Publishing place	Hoboken, NJ
Document type	Book
Note	Includes bibliogtaphical reference and index
ISBN	0471746975 ; 9780471746973
Database	Library catalogue of the German National Library of Science and Technology (TIB), Hannover

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