A generalized confusion matrix for assessing area estimates from remotely sensed data

Lewis, Hugh G.; Brown, M.

doi:10.1080/01431160152558332

Cited by 189 publications

(99 citation statements)

References 24 publications

(38 reference statements)

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“…Methods for assessing the accuracy of fuzzy classifications were suggested by Fisher and Pathirana (1991) based on estimating the portions of any pixel occupied by candidate land covers and were extended by Lewis and Brown (2001). Gopal and Woodcock (1994) suggested a fuzzy error matrix that incorporated linguistic descriptors to evaluate the land cover attributes associated with each sample location against each category in the classification scheme.…”

Section: Introductionmentioning

confidence: 99%

Spatial analysis of remote sensing image classification accuracy

Comber

Fisher

Brunsdon

et al. 2012

Remote Sensing of Environment

190

119

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The error matrix is the most common way of expressing the accuracy of remote sensing image classifications, such as land cover. However, it and the measures that can be calculated from it have been criticised for not providing any indication of the spatial distribution of errors. Other research has identified the need for methods to analyse the spatial non-stationarity of error and to visualise the spatial variation in classification uncertainty. This research uses geographically weighted approaches to model the spatial variations in the accuracy of both (crisp) Boolean and (soft) fuzzy land cover classes. Remotely sensed data were classified using a maximum likelihood classifier and a fuzzy classifier to predict Boolean and fuzzy land cover classes respectively. Field data were collected at sub-pixel locations and used to generate soft and crisp validation data. A Geographically Weighted Regression was used to analyse spatial variations in the relationships between observations of Boolean land cover in the field and land cover classified from remote sensing imagery. A geographically weighted difference measure was used to analyse spatial variations in fuzzy land cover accuracy. Maps of the spatial distribution of accuracy were created for fuzzy and Boolean classes. This research demonstrates that data collected as part of a standard remote sensing validation exercise can be used to estimate mapped, spatial distributions of accuracy that would augment standard accuracy measures reported in the error matrix. It suggests that geographically weighted approaches, and the spatially explicit representations of accuracy they support, offer the opportunity to report land cover accuracy in a more informative way.

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Section: Introductionmentioning

confidence: 99%

Spatial analysis of remote sensing image classification accuracy

Comber

Fisher

Brunsdon

et al. 2012

Remote Sensing of Environment

190

119

View full text Add to dashboard Cite

show abstract

“…Given this issue, much effort has been made to generalize the conventional error matrix and adapt it for use in soft classification. Until now, various approaches have been advanced for the sub-pixel error matrix based on a number of mathematical theories such as minimum operator [5], multiplication operator [6] and composite operator [7,8]. From the soft error matrix, a number of descriptive and analytical statistical measures can be calculated including overall accuracy (oa), kappa coefficient (kappa), user's accuracy (ua) and producer's accuracy (pa) [4].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Positional Errors on Soft Classification Accuracy Assessment: A Simulation Analysis

Congalton

Pan

2015

Remote Sensing

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Abstract:Validating or accessing the accuracy of soft classification maps has rapidly developed over the past few years. This assessment employs a soft error matrix as generalized from the traditional, hard classification error matrix. However, the impact of positional error on the soft classification is uncertain and whether the well-accepted half-pixel registration accuracy is suitable for the soft classification accuracy assessment is unknown. In this paper, a simulation analysis was conducted to examine the influence of positional error on the overall accuracy (OA) and kappa in soft classification accuracy assessment under different landscape conditions (i.e., spatial characteristics and spatial resolutions). Results showed that with positional error ranging from 0 to 3 soft pixels, the OA-error varied from 0 to 44.6 percent while the kappa-error varied from 0 to 93.7 percent. Landscape conditions with smaller mean patch size (MPS) and greater fragmentation produced greater positional error impact on the accuracy measures at spatial resolutions of 1 and 2 unit distances. However, this trend did not hold for spatial resolutions of 5 and 10 unit distances. A half of a pixel was not sufficient to keep the overall accuracy error and kappa error under 10 percent. The results indicate that for soft classification accuracy assessment the requirement for registration accuracy is higher and depends greatly on the landscape characteristics. There is a great need to consider positional error for validating soft classification maps of different spatial resolutions. OPEN ACCESSRemote Sens. 2015, 7 580

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“…Unfortunately, confusion matrices aren't readily applicable to spectral unmixing and AMRD. Methods have been proposed to generalize confusion matrices for use with AMRD [29], but these methods do not allow straight-forward comparison of individual matrix elements, and essentially amount to comparing total RD area to total unmixed area per class.…”

Section: Comparison Of Unmixing Results and Amrdmentioning

confidence: 99%

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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A generalized confusion matrix for assessing area estimates from remotely sensed data

Cited by 189 publications

References 24 publications

Spatial analysis of remote sensing image classification accuracy

Spatial analysis of remote sensing image classification accuracy

The Impact of Positional Errors on Soft Classification Accuracy Assessment: A Simulation Analysis

Application of Abundance Map Reference Data for Spectral Unmixing

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