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Coastal Environments: Remote Sensing
Published in Yeqiao Wang, Coastal and Marine Environments, 2020
Hyperspectral technology brings new insights about remote sensing of coastal environments. Hyperion sensor onboard of EO-1 satellite is a representative space-borne hyperspectral system. The Hyperion Imaging Spectrometer collects data in 30-m ground sample distance over a 7.5-km swath and provides 10-nm (sampling interval) 220 contiguous spectral bands of the solar reflected spectrum from 400 nm to 2,500 nm. Airborne Visible Infrared Imaging Spectrometer (AVIRIS) is a representative airborne hyperspectral sensor system. The AVIRIS whiskbroom scanner collects data in the same spectral interval and range as the Hyperion system but with 20-m spatial resolution. Hyperspectral remote sensing has advantages in coastal wetlands characterization due to its large number of narrow, contiguous spectral bands as well as high horizontal resolution from airborne platforms. It has been used to map habitat heterogeneity[42] to determine plant cover distribution in salt marshes.[43] Hyperspectral images have been used to separate vigor types by detecting slight differences in coloration due to stress factors, infestation, or displacement by invading species.[44] A study mapped the onset and progression of coastal Spartina alterniflora marsh dieback using hyperspectral image data at the plant leaf, canopy, and satellite levels without a priori information on where, when, or how long the dieback had proceeded.[45] Hyperspectral data offer an enhanced ability to determine dieback onset and track progression.
Introduction to Hyperspectral Satellites
Published in Shen-En Qian, Hyperspectral Satellites and System Design, 2020
Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) is the best known airborne hyperspectral imager in the community of terrestrial hyperspectral remote sensing. It is the first hyperspectral imager that measures the solar reflected spectrum from 400 nm to 2500 nm at 10-nm spectral sampling intervals. It offers best calibrated hyperspectral data resulting from its very high SNR and carefully designed calibration system and well-implemented calibration procedure. As an operational airborne imaging spectrometer, AVIRIS is the primary provider of hyperspectral data to the research community since 1987. JPL has organized over 10 workshops on AVIRIS since 1988. Numerous scientific papers related to AVIRIS have been published.
A Novel Dasr Approach for Reconstruction of Hyper-Spectral Images
Published in Durgesh Kumar Mishra, Nilanjan Dey, Bharat Singh Deora, Amit Joshi, ICT for Competitive Strategies, 2020
K. S. Gunasheela, H. S. Prasantha
In this section, the simulation results of the proposed DASR approach for reconstruction of HSIs is presented. The proposed approach is compared with some state-of-art techniques of HSI compressive sensing techniques. The simulation is done using Matlab 2016b. Here, the Hyperspectral dataset [13] is considered as experimental data, which allows for detailed comparative and quantitative study of the proposed approach. There are three scenes of cuprite. In this study, scene two of the cuprite images is considered for experimentation. These scenes are captured by the AVIRIS [13] sensor. The HSI data collected by AVIRIS sensor consists of 224 spectral bands and spectral resolution varies from 0.4 to 2.5. The spatial resolution at each pixel is 20m.
Feature reduction of hyperspectral image for classification
Published in Journal of Spatial Science, 2022
Rashedul Islam, Boshir Ahmed, Ali Hossain
In recent years, hyperspectral image (HSI) analysis has become popular in remote sensing for its wide variety of application areas such as ground cover analysis, geographical information system, data mining, military surveillance and so on (Transon et al. 2018). HSI is a high volume datacube usually consisting of hundreds of many narrow and contiguous spectral image bands. Each image band is collected by the reflectance of ground objects at an individual wavelength with a fine spectral resolution. For example, the NASA AVIRIS sensor collects 224 spectral responses as contiguous image bands which usually range from 0.4 µm to 2.4 µm which covers visible light to near-infrared region of the electromagnetic spectrum (Mohan and Porwal 2015). Moreover, this fine spectral resolution allows researchers to analyze ground objects and has become a popular research topic. Here, each spectral band is known as a feature for classification as long as they contain individual responses of the ground surface (Richards and Jia 2006). Generally, HSI can be represented as a three-dimensional (3D) datacube which contains two-dimensional (2D) spatial information and 1D spectral information about the ground (Figure 1). The complete data set of HSI can be represented as H × W × P, where H, W and P represent the number of rows, columns and spectral bands, respectively (Zabalza et al. 2014).
Lensless broadband diffractive imaging with improved depth of focus: wavefront modulation by multilevel phase masks
Published in Journal of Modern Optics, 2019
Vladimir Katkovnik, Mykola Ponomarenko, Karen Egiazarian
In our experiments, we use high-quality remote sensing images from AVIRIS (Airborne Visible Infrared Imaging Spectrometer (AVIRIS), NASA .1 Overall, the AVIRIS data set consists of 224 channel images with nm step between the spectral channels. As the ground truth data, we selected four images available free of charge for scientific research: Moffett Field, Cuprite, Lunar Lake and Low Altitude. From each image, we cut off fragments full of multicoloured sharp details and took 28 channels of the range nm with the wavelength step.
Dimensionality reduction and classification for hyperspectral image based on robust supervised ISOMAP
Published in Journal of Industrial and Production Engineering, 2022
Shengfeng Ding, Colin Arthur Keal, Lizhou Zhao, Dan Yu
In order to evaluate the performance of Robust Supervised ISOMAP, Indian Pines AVIRIS image [45] is used. AVIRIS image has 220 bands, 145 pixels *145 pixels size, 20 m * 20 m spatial resolution, 2.5 ~ 0.4 µm wavelength range, 10 nm spectral resolution. The data set contains 16 kinds of real ground object, including 10,366 labeled samples. Seven kinds of object samples don’t be used for test because of they are too few, the other 9 kinds of object have 9345 samples. Figure 4 shows false color composite image constructed of band 50,27,17. The experimental data is shown in Table 1.