TY - CHAP
T1 - Compressive sensing for reconstruction, classification, and detection of hyperspectral images
AU - Zhang, Bing
AU - Li, Wei
AU - Gao, Lianru
AU - Sun, Xu
N1 - Publisher Copyright:
© 2017 by Taylor & Francis Group, LLC.
PY - 2017/1/1
Y1 - 2017/1/1
N2 - Hyperspectral imagery (HSI) acquired by remote-sensing systems (e.g., a spaceborne or an airborne sensor) typically records reflectance values over a wide region of the electromagnetic spectrum [1,2]. Each pixel in an HSI represents many contiguous and narrow spectral bands (e.g., a spectral range of 0.4 to 2.4 µm). This enables HSI to potentially provide rich information about the materials in the image scene. HSI has become a widely available modality used in a wide variety of applications, including urban-growth analysis, biological and chemical detection, environmental monitoring, mineral exploration, etc.
AB - Hyperspectral imagery (HSI) acquired by remote-sensing systems (e.g., a spaceborne or an airborne sensor) typically records reflectance values over a wide region of the electromagnetic spectrum [1,2]. Each pixel in an HSI represents many contiguous and narrow spectral bands (e.g., a spectral range of 0.4 to 2.4 µm). This enables HSI to potentially provide rich information about the materials in the image scene. HSI has become a widely available modality used in a wide variety of applications, including urban-growth analysis, biological and chemical detection, environmental monitoring, mineral exploration, etc.
UR - http://www.scopus.com/inward/record.url?scp=85045572022&partnerID=8YFLogxK
U2 - 10.1201/9781315154626
DO - 10.1201/9781315154626
M3 - Chapter
AN - SCOPUS:85045572022
SN - 9781498774376
SP - 273
EP - 297
BT - Compressive Sensing of Earth Observations
PB - CRC Press
ER -