Airborne hyperspectral sensor systems

Birk, Ronald J.; McCord, T. B.

doi:10.1109/naecon.1994.332990

Cited by 5 publications

(6 citation statements)

References 8 publications

(6 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As most imaging spectrometers critically sample spectra, their spectral resolution would be twice their BP width by this definition. For more information on existing hyperspectral systems, see Birk and McCord [1994], Clark [1999], and Kruse [1999].…”

Section: Introductionmentioning

confidence: 99%

Effects of spectrometer band pass, sampling, and signal‐to‐noise ratio on spectral identification using the Tetracorder algorithm

et al. 2003

View full text Add to dashboard Cite

[1] Estimates of spectrometer band pass, sampling interval, and signal-to-noise ratio required for identification of pure minerals and plants were derived using reflectance spectra convolved to AVIRIS, HYDICE, MIVIS, VIMS, and other imaging spectrometers. For each spectral simulation, various levels of random noise were added to the reflectance spectra after convolution, and then each was analyzed with the Tetracorder spectral identification algorithm . The outcome of each identification attempt was tabulated to provide an estimate of the signal-to-noise ratio at which a given percentage of the noisy spectra were identified correctly. Results show that spectral identification is most sensitive to the signal-to-noise ratio at narrow sampling interval values but is more sensitive to the sampling interval itself at broad sampling interval values because of spectral aliasing, a condition when absorption features of different materials can resemble one another. The band pass is less critical to spectral identification than the sampling interval or signal-to-noise ratio because broadening the band pass does not induce spectral aliasing. These conclusions are empirically corroborated by analysis of mineral maps of AVIRIS data collected at Cuprite, Nevada, between 1990 and 1995, a period during which the sensor signal-to-noise ratio increased up to sixfold. There are values of spectrometer sampling and band pass beyond which spectral identification of materials will require an abrupt increase in sensor signal-to-noise ratio due to the effects of spectral aliasing. Factors that control this threshold are the uniqueness of a material's diagnostic absorptions in terms of shape and wavelength isolation, and the spectral diversity of the materials found in nature and in the spectral library used for comparison. Array spectrometers provide the best data for identification when they critically sample spectra. The sampling interval should not be broadened to increase the signal-to-noise ratio in a photon-noise-limited system when high levels of accuracy are desired. It is possible, using this simulation method, to select optimum combinations of band-pass, sampling interval, and signal-to-noise ratio values for a particular application that maximize identification accuracy and minimize the volume of imaging data.

show abstract

Section: Introductionmentioning

confidence: 99%

Effects of spectrometer band pass, sampling, and signal‐to‐noise ratio on spectral identification using the Tetracorder algorithm

et al. 2003

View full text Add to dashboard Cite

show abstract

“…This approach takes into account the spectral variability of the endmembers modeled as random vectors. A new SPTD strategy has been derived by modeling the pixel, under 1 H hypothesis, through the SMM and by assuming the target signature deterministic and known. The algorithm is based on the LSME estimate of the target fill factor in the PUT and the detector statistic is expressed in closed form, as opposite to the SMM based algorithms derived by adopting the MAP and the GLRT approach.…”

Section: Discussionmentioning

confidence: 99%

“…Hyperspectral sensors provide a high resolution spectral coverage of the observed radiation with hundreds of narrow and contiguous bands [1]. They allow the discrimination of different natural and man-made materials on the basis of their unique spectral signature.…”

Section: Introductionmentioning

confidence: 99%

Adaptive sub-pixel target detection in hyperspectral images based on the stochastic mixing model

et al. 2005

View full text Add to dashboard Cite

This paper addresses the problem of sub-pixel target detection in hyperspectral images assuming that the target spectral signature is deterministic and known. Hyperspectral image pixels are frequently a combination or mixture of disparate materials or components. The need of a quantitative pixel decomposition arises in many civilian and military applications such as material classification, anomaly and target detection. The Linear Mixing Model (LMM) is a widely used method in hyperspectral data analysis. It represents a mixed pixel as the sum of the spectra of known pure materials, called endmembers, weighted by their relative concentrations called abundance coefficients. However, the LMM does not take into account the natural spectral variability of the endmembers. This variability is well represented by the Stochastic Mixing Model (SMM), which provides a model for describing both mixed pixels in the scene and endmember spectral variations through a statistical model. Modeling the background spectrum as a Gaussian random vector with known mean spectrum and unknown covariance matrix, a novel SMM based Detector (ASMMD) is derived in this paper. The ASMMD theoretical performances are evaluated in a case study referring to actual conditions. The analysis is conducted by estimating the ASMMD parameters on an experimental data set acquired by the AVIRIS hyperspectral sensor and the results are compared with the ones achieved by the Adaptive Matched Subspace Detector (AMSD), based on the LMM

show abstract

“…Airborne hyperspectral imagery provides data with a high spectral and spatial resolution that may be useful to map culture land which has different types of soils and crops over a large range [ 12 ]. Hbirkou et al [ 13 ] predicted SOC over a small-scale bare and fine agricultural soil using the data obtained from aircraft-mounted hyperspectral sensor HyMap and achieved prediction accuracy of RMSEP = 0.76 g kg −1 and RPD = 2.08.…”

Section: Introductionmentioning

confidence: 99%

Soil Nutrient Estimation and Mapping in Farmland Based on UAV Imaging Spectrometry

Yang

Bao

et al. 2021

Sensors

View full text Add to dashboard Cite

Soil nutrient is one of the most important properties for improving farmland quality and product. Imaging spectrometry has the potential for rapid acquisition and real-time monitoring of soil characteristics. This study aims to explore the preprocessing and modeling methods of hyperspectral images obtained from an unmanned aerial vehicle (UAV) platform for estimating the soil organic matter (SOM) and soil total nitrogen (STN) in farmland. The results showed that: (1) Multiplicative Scattering Correction (MSC) performed better in reducing image scattering noise than Standard Normal Variate (SNV) transformation or spectral derivatives, and it yielded a result with higher correlation and lower signal-to-noise ratio; (2) The proposed feature selection method combining Successive Projections Algorithm (SPA) and Competitive Adaptive Reweighted Sampling algorithm (CARS), could provide selective preference for hyperspectral bands. Exploiting this method, 24 and 22 feature bands were selected for SOM and STN estimation, respectively; (3) The particle swarm optimization (PSO) algorithm was employed to obtain optimized input weights and bias values of the extreme learning machine (ELM) model for more accurate prediction of SOM and STN. The improved PSO-ELM model based on the selected preference bands achieved higher prediction accuracy (R2 of 0.73 and RPD of 1.91 for SOM, R2 of 0.63, and RPD of 1.53 for STN) than support vector machine (SVM), partial least squares regression (PLSR), and the ELM model. This study provides an important guideline for monitoring soil nutrient for precision agriculture with imaging spectrometry.

show abstract

Airborne hyperspectral sensor systems

Cited by 5 publications

References 8 publications

Effects of spectrometer band pass, sampling, and signal‐to‐noise ratio on spectral identification using the Tetracorder algorithm

Effects of spectrometer band pass, sampling, and signal‐to‐noise ratio on spectral identification using the Tetracorder algorithm

Adaptive sub-pixel target detection in hyperspectral images based on the stochastic mixing model

Soil Nutrient Estimation and Mapping in Farmland Based on UAV Imaging Spectrometry

Contact Info

Product

Resources

About