Validation of Abundance Map Reference Data for Spectral Unmixing

Williams, McKay D.; Parody, Robert; Fafard, Alexander; Kerekes, John P.; Aardt, Jan van

doi:10.3390/rs9050473

Cited by 10 publications

(17 citation statements)

References 25 publications

(34 reference statements)

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“…In order to determine the accuracy of AMRD generated using the RSRD technique, we conducted an extensive validation study of AMRD for three remote sensing scenes [8]. This paper continues our work with AMRD by applying our previously generated and validated AMRD to specific coarse scale airborne IS imagery.…”

Section: Introductionmentioning

confidence: 99%

“…We recommend reviewing the validation paper [8] for detailed information on the validation process; however, we will mention the effort briefly here.…”

Section: Validated Abundance Map Reference Datamentioning

confidence: 99%

“…Unmixing was performed using these initial ground cover classes, with resulting abundances being combined to produce abundances for our final cover classes. Further details are available in our AMRD validation paper [8]. …”

Section: Spectral Unmixingmentioning

confidence: 99%

“…Abundance map reference data (AMRD) are a special type of RD, where each pixel is allowed to be a mixture of the various ground cover classes. AMRD are designed to quantitatively evaluate the performance of spectral unmixing algorithms [7,8]. However, we are not aware of any similarly well-known and widely-available AMRD scenes for airborne imaging spectrometer (IS) data.…”

Section: Introductionmentioning

confidence: 99%

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Application of Abundance Map Reference Data for Spectral Unmixing

2017

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Reference data ("ground truth") maps have traditionally been used to assess the accuracy of classification algorithms. These maps typically classify pixels or areas of imagery as belonging to a finite number of ground cover classes, but do not include sub-pixel abundance estimates; therefore, they are not sufficiently detailed to directly assess the performance of spectral unmixing algorithms. Our research aims to efficiently generate, validate, and apply abundance map reference data (AMRD) to airborne remote sensing scenes. Scene-wide AMRD for this study were generated using the remotely sensed reference data (RSRD) technique, which spatially aggregates classification or unmixing results from fine scale imagery (e.g., 1-m GSD) to co-located coarse scale imagery (e.g., 10-m GSD or larger). Validation of the accuracy of these methods was previously performed for generic 10 m × 10 m coarse scale imagery, resulting in AMRD with known accuracy. The purpose of this paper was to apply this previously validated AMRD to specific examples of airborne coarse scale imagery. Application of AMRD involved three main parts: (1) spatial alignment of coarse and fine scale imagery; (2) aggregation of fine scale abundances to produce coarse scale imagery specific AMRD; and (3) demonstration of comparisons between coarse scale unmixing abundances and AMRD. Spatial alignment was performed using our new scene-wide spectral comparison (SWSC) algorithm, which aligned imagery with accuracy approaching the distance of a single fine scale pixel. We compared simple rectangular aggregation to coarse sensor point-spread function (PSF) aggregation, and found that PSF returned lower error, but that rectangular aggregation more accurately estimated true AMRD at ground level. We demonstrated various metrics for comparing unmixing results to AMRD, including several new techniques which adjust for known error in the reference data itself. These metrics indicated that fully constrained linear unmixing of AVIRIS imagery across all three scenes returned an average error of 10.83% per class and pixel. Our reference data research has demonstrated a viable methodology to efficiently generate, validate, and apply AMRD to specific examples of airborne remote sensing imagery, thereby enabling direct quantitative assessment of spectral unmixing performance.

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Section: Introductionmentioning

confidence: 99%

“…We recommend reviewing the validation paper [8] for detailed information on the validation process; however, we will mention the effort briefly here.…”

Section: Validated Abundance Map Reference Datamentioning

confidence: 99%

Section: Spectral Unmixingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of Abundance Map Reference Data for Spectral Unmixing

2017

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“…The wealth of spectral information in HSIs has opened new perspectives in different applications, such as target detection, spectral unmixing, object classification, and matching [1][2][3][4][5][6][7][8][9][10][11][12][13]. The underlying assumption in object classification techniques is that each pixel comprises the response of only one material.…”

Section: Introductionmentioning

confidence: 99%

Sparse Unmixing of Hyperspectral Data with Noise Level Estimation

Mei

et al. 2017

Remote Sensing

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Abstract:Recently, sparse unmixing has received particular attention in the analysis of hyperspectral images (HSIs). However, traditional sparse unmixing ignores the different noise levels in different bands of HSIs, making such methods sensitive to different noise levels. To overcome this problem, the noise levels at different bands are assumed to be different in this paper, and a general sparse unmixing method based on noise level estimation (SU-NLE) under the sparse regression framework is proposed. First, the noise in each band is estimated on the basis of the multiple regression theory in hyperspectral applications, given that neighboring spectral bands are usually highly correlated. Second, the noise weighting matrix can be obtained from the estimated noise. Third, the noise weighting matrix is integrated into the sparse regression unmixing framework, which can alleviate the impact of different noise levels at different bands. Finally, the proposed SU-NLE is solved by the alternative direction method of multipliers. Experiments on synthetic datasets show that the signal-to-reconstruction error of the proposed SU-NLE is considerably higher than those of the corresponding traditional sparse regression unmixing methods without noise level estimation, which demonstrates the efficiency of integrating noise level estimation into the sparse regression unmixing framework. The proposed SU-NLE also shows promising results in real HSIs.Keywords: alternative direction method of multipliers (ADMM); hyperspectral image (HSI); sparse unmixing method based on noise level estimation (SU-NLE)

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