1. Hyperspectral Imaging and Applications.
- Author
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Chang, Chein-I, Chang, Chein-I, Song, Meiping, Wu, Chao-Cheng, and Zhang, Junping
- Subjects
History of engineering & technology ,Technology: general issues ,90° yaw imaging ,AHS ,AVIRIS ,Africa ,Dunhuang site ,Gram-Schmidt orthogonalization ,HyMap ,KSVD ,Otsu's method ,SVM ,adaptive window ,agroforestry ,airborne laser scanning ,algebraic multigrid methods ,anomaly detection ,band expansion process (BEP) ,band grouping ,band selection ,band selection (BS) ,band subset selection (BSS) ,biodiversity ,class imbalance ,classification ,composite kernel ,constrained energy minimization ,constrained energy minimization (CEM) ,correlation band expansion process (CBEP) ,data fusion ,data integration ,data unmixing ,data-guided constraints ,deep belief networks ,deep learning ,deep pipelined background statistics ,endmember extraction ,ensemble learning ,evenness ,fire severity ,graph ,hashing ensemble ,hierarchical feature ,high-level synthesis ,hyperspectral ,hyperspectral classification ,hyperspectral compression ,hyperspectral detection ,hyperspectral image ,hyperspectral image (HSI) ,hyperspectral image classification ,hyperspectral imagery ,hyperspectral images (HSIs) ,hyperspectral imaging ,hyperspectral pansharpening ,hyperspectral unmixing ,image enhancement ,image fusion ,imaging spectroscopy ,in situ measurements ,intrinsic image decomposition ,irradiance-based method ,iterative CEM (ICEM) ,iterative algorithm ,label propagation ,linearly constrained minimum variance (LCMV) ,local abundance ,local summation RX detector (LS-RXD) ,lossy compression ,machine learning ,mineral mapping ,minimum noise fraction ,multiscale spatial information ,multiscale union regions adaptive sparse representation (MURASR) ,nonlinear band expansion (NBE) ,nonnegative matrix factorization ,nuclear norm ,on-board compression ,optical spectral region ,orthogonal projections ,panchromatic ,panchromatic image ,parallel processing ,peatland ,progressive sample processing (PSP) ,prototype space ,raw material ,real-time processing ,recursive anomaly detection ,reflectance-based method ,remote sensing ,rolling guidance filtering (RGF) ,rotation forest ,semi-supervised learning ,semi-supervised local discriminant analysis ,sequential LCMV-BSS (SQ LCMV-BSS) ,sliding window ,sparse coding ,sparse unmixing ,sparseness ,spectral mixture analysis ,spectral variability ,spectral-spatial classification ,sprout detection ,structure tensor ,successive LCMV-BSS (SC LCMV-BSS) ,superpixel ,target detection ,terrestrial hyperspectral imaging ,texture feature enhancement ,thermal infrared spectral region ,tree species ,tree-based ensemble ,vegetation type ,vicarious calibration ,vineyard ,water stress ,weighted fusion ,weighted least squares filter - Abstract
Summary: 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 with the number of papers published in every category included in its open parenthesis. 1. Data Unmixing (2 papers)2. Spectral variability (2 papers)3. Target Detection (3 papers)4. Hyperspectral Image Classification (6 papers)5. Band Selection (2 papers)6. Data Fusion (2 papers)7. Applications (8 papers) Under every category each paper is briefly summarized by a short description so that readers can quickly grab its content to find what they are interested in.