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Hyperspectral Remote Sensing

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Presentation on theme: "Hyperspectral Remote Sensing"— Presentation transcript:

1 Hyperspectral Remote Sensing

2 Hyperspectral Sensing
Multiple channels (50+) at fine spectral resolution (e.g., 5 nm in width) across the full spectrum from VIS-NIR-MIR to capture full reflectance spectrum and distinguish narrow absorption features

3 Reflectance from green plant leaves
Chlorophyll absorbs in and nm region. The blue region overlaps with carotenoid absorption, so focus is on red region. Peak reflectance in leaves in near infrared (.7-1.2um) up to 60% of infrared energy per leaf is scattered up or down due to cell wall size, shape, leaf condition (age, stress, disease), etc. Reflectance in Mid IR (2-4um) influenced by water content- water absorbs IR energy, so live leaves reduce mid IR return

4 Hand-held Spectroradiometer
Calibrated vs “dark” vs. “bright” reference standard provided (spectralon white panel - #6 in image) Can use “passive” sensor to record reflected sunlight or “active” illuminated sensor clip (#4)

5 AVIRIS:Airborne Visible InfraRed Imaging Spectrometer

6 Hyperspectral sensing: AVIRIS

7 Compact Airborne Spectrographic Imager (CASI)
Hyperspectral: 288 channels between mm; each channel 0.018mm wide Spatial resolution depends on flying height of aircraft and number of channels acquired CASI 550 For more info:

8 EO-1: Hyperion The Hyperion collects 220 unique spectral channels ranging from to micrometers with a 10-nm bandwidth. The instrument operates in a pushbroom fashion, with a spatial resolution of 30 meters for all bands. The standard scene width is 7.7 kilometers. Standard scene length is 42 kilometers, with an optional increased scene length of 185 kilometers More info:

9 EO-1 ALI & Hyperion designed to work in tandem

10 Hyperion over New Jersey

11 Hyperion Image EO1H0140312004120110PY 2004/04/29
R 800- G 650- B 550 Fallow field Active crop

12 Hyperion Image EO1H0140312004184110PX 2004/07/02
R 800- G 650- B 550 Conifer forest Deciduous forest

13 Hyperspectral Sensing: Analytical Techniques
Data Dimensionality and Noise Reduction: MNF Ratio Indices Derivative Spectroscopy Spectral Angle or Spectroscopic Library Matching Subpixel (linear spectral unmixing) analysis

14 http://www.ajol.info/index.php/wsa/article/viewFi le/49049/35397
tml Open Penn State RS class: 258 pages, if you need help sleeping: ers/BookChapters/2012.EUFAR.Hyperspectral.pdf

15 31 pages. Looks good. http://www. umbc
31 pages. Looks good. ers/Journals/2013.GRSM.Hyperspectral.pdf


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