Hyperspectral Imaging and Applications
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"...
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| Year of Publication: | 2022 |
| Language: | English |
| Physical Description: | 1 electronic resource (632 p.) |
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| 100 | 1 | |a Chang, Chein-I |4 edt | |
| 245 | 1 | 0 | |a Hyperspectral Imaging and Applications |
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| 300 | |a 1 electronic resource (632 p.) | ||
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| 520 | |a 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. | ||
| 546 | |a English | ||
| 650 | 7 | |a Technology: general issues |2 bicssc | |
| 650 | 7 | |a History of engineering & technology |2 bicssc | |
| 653 | |a biodiversity | ||
| 653 | |a peatland | ||
| 653 | |a vegetation type | ||
| 653 | |a classification | ||
| 653 | |a hyperspectral | ||
| 653 | |a in situ measurements | ||
| 653 | |a hyperspectral image (HSI) | ||
| 653 | |a multiscale union regions adaptive sparse representation (MURASR) | ||
| 653 | |a multiscale spatial information | ||
| 653 | |a imaging spectroscopy | ||
| 653 | |a airborne laser scanning | ||
| 653 | |a minimum noise fraction | ||
| 653 | |a class imbalance | ||
| 653 | |a Africa | ||
| 653 | |a agroforestry | ||
| 653 | |a tree species | ||
| 653 | |a hyperspectral unmixing | ||
| 653 | |a endmember extraction | ||
| 653 | |a band selection | ||
| 653 | |a spectral variability | ||
| 653 | |a prototype space | ||
| 653 | |a ensemble learning | ||
| 653 | |a rotation forest | ||
| 653 | |a semi-supervised local discriminant analysis | ||
| 653 | |a optical spectral region | ||
| 653 | |a thermal infrared spectral region | ||
| 653 | |a mineral mapping | ||
| 653 | |a data integration | ||
| 653 | |a HyMap | ||
| 653 | |a AHS | ||
| 653 | |a raw material | ||
| 653 | |a remote sensing | ||
| 653 | |a nonnegative matrix factorization | ||
| 653 | |a data-guided constraints | ||
| 653 | |a sparseness | ||
| 653 | |a evenness | ||
| 653 | |a hashing ensemble | ||
| 653 | |a hierarchical feature | ||
| 653 | |a hyperspectral classification | ||
| 653 | |a band expansion process (BEP) | ||
| 653 | |a constrained energy minimization (CEM) | ||
| 653 | |a correlation band expansion process (CBEP) | ||
| 653 | |a iterative CEM (ICEM) | ||
| 653 | |a nonlinear band expansion (NBE) | ||
| 653 | |a Otsu’s method | ||
| 653 | |a sparse unmixing | ||
| 653 | |a local abundance | ||
| 653 | |a nuclear norm | ||
| 653 | |a hyperspectral detection | ||
| 653 | |a target detection | ||
| 653 | |a sprout detection | ||
| 653 | |a constrained energy minimization | ||
| 653 | |a iterative algorithm | ||
| 653 | |a adaptive window | ||
| 653 | |a hyperspectral imagery | ||
| 653 | |a recursive anomaly detection | ||
| 653 | |a local summation RX detector (LS-RXD) | ||
| 653 | |a sliding window | ||
| 653 | |a band selection (BS) | ||
| 653 | |a band subset selection (BSS) | ||
| 653 | |a hyperspectral image classification | ||
| 653 | |a linearly constrained minimum variance (LCMV) | ||
| 653 | |a successive LCMV-BSS (SC LCMV-BSS) | ||
| 653 | |a sequential LCMV-BSS (SQ LCMV-BSS) | ||
| 653 | |a vicarious calibration | ||
| 653 | |a reflectance-based method | ||
| 653 | |a irradiance-based method | ||
| 653 | |a Dunhuang site | ||
| 653 | |a 90° yaw imaging | ||
| 653 | |a terrestrial hyperspectral imaging | ||
| 653 | |a vineyard | ||
| 653 | |a water stress | ||
| 653 | |a machine learning | ||
| 653 | |a tree-based ensemble | ||
| 653 | |a progressive sample processing (PSP) | ||
| 653 | |a real-time processing | ||
| 653 | |a image fusion | ||
| 653 | |a hyperspectral image | ||
| 653 | |a panchromatic image | ||
| 653 | |a structure tensor | ||
| 653 | |a image enhancement | ||
| 653 | |a weighted fusion | ||
| 653 | |a spectral mixture analysis | ||
| 653 | |a fire severity | ||
| 653 | |a AVIRIS | ||
| 653 | |a deep belief networks | ||
| 653 | |a deep learning | ||
| 653 | |a texture feature enhancement | ||
| 653 | |a band grouping | ||
| 653 | |a hyperspectral compression | ||
| 653 | |a lossy compression | ||
| 653 | |a on-board compression | ||
| 653 | |a orthogonal projections | ||
| 653 | |a Gram–Schmidt orthogonalization | ||
| 653 | |a parallel processing | ||
| 653 | |a anomaly detection | ||
| 653 | |a sparse coding | ||
| 653 | |a KSVD | ||
| 653 | |a hyperspectral images (HSIs) | ||
| 653 | |a SVM | ||
| 653 | |a composite kernel | ||
| 653 | |a algebraic multigrid methods | ||
| 653 | |a hyperspectral pansharpening | ||
| 653 | |a panchromatic | ||
| 653 | |a intrinsic image decomposition | ||
| 653 | |a weighted least squares filter | ||
| 653 | |a spectral-spatial classification | ||
| 653 | |a label propagation | ||
| 653 | |a superpixel | ||
| 653 | |a semi-supervised learning | ||
| 653 | |a rolling guidance filtering (RGF) | ||
| 653 | |a graph | ||
| 653 | |a deep pipelined background statistics | ||
| 653 | |a high-level synthesis | ||
| 653 | |a data fusion | ||
| 653 | |a data unmixing | ||
| 653 | |a hyperspectral imaging | ||
| 776 | |z 3-03921-522-1 | ||
| 776 | |z 3-03921-523-X | ||
| 700 | 1 | |a Song, Meiping |4 edt | |
| 700 | 1 | |a Zhang, Junping |4 edt | |
| 700 | 1 | |a Wu, Chao-Cheng |4 edt | |
| 700 | 1 | |a Chang, Chein-I |4 oth | |
| 700 | 1 | |a Song, Meiping |4 oth | |
| 700 | 1 | |a Zhang, Junping |4 oth | |
| 700 | 1 | |a Wu, Chao-Cheng |4 oth | |
| 906 | |a BOOK | ||
| ADM | |b 2023-12-15 06:00:30 Europe/Vienna |f system |c marc21 |a 2022-08-13 21:17:26 Europe/Vienna |g false | ||
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