remote sensing classification accuracy. 1. select test areas ► selecte test areas in an image to...
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Remote SensingRemote Sensing
Classification AccuracyClassification Accuracy
1. Select Test Areas1. Select Test Areas
► Selecte test areas in an image to evaluate Selecte test areas in an image to evaluate the accuracy of a classificationthe accuracy of a classification
► Test areas should be representative Test areas should be representative categorically and geographicallycategorically and geographically
► Sampling methods: uniform wall-to-wall, Sampling methods: uniform wall-to-wall, random, stratified random sampling random, stratified random sampling
► Sample size: 50 - 100 pixels each category Sample size: 50 - 100 pixels each category
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2. Error Assessment2. Error Assessment
► A classification is not complete until its A classification is not complete until its accuracy is assessedaccuracy is assessed
► Error matrixError matrix► KHAT statisticsKHAT statistics
Error MatrixError Matrix
► Also called confusion matrix and Also called confusion matrix and contingency table contingency table
► Compares the ground truth and the results Compares the ground truth and the results of the of the classification for the test areasclassification for the test areas
► Can be used to evaluate the result of Can be used to evaluate the result of classifying the training set pixels and the classifying the training set pixels and the results of classifying the actual full-sceneresults of classifying the actual full-scene
Error MatrixError Matrix Classified Reference Data
Data Water Sand Forest Urban Corn Hay Row Total Water 480 0 5 0 0 0 485 Sand 0 52 0 20 0 0 72 Forest 0 0 313 40 0 0 353 Urban 0 16 0 126 0 0 142 Corn 0 0 0 38 342 79 459 Hay 0 0 38 24 60 359 481Col Total 480 68 356 248 402 438 1992
Diagonal cells are correctly classified pixels Diagonal cells are correctly classified pixels
correctly classified pixels 1672 correctly classified pixels 1672 Overall accuracy = ------------------------------- = ------- = 84%Overall accuracy = ------------------------------- = ------- = 84% total pixels evaluated 1992 total pixels evaluated 1992
Error MatrixError Matrix
In this case, the non-diagonal column cells are omission In this case, the non-diagonal column cells are omission errors errors e.g. omission error for forest = 43/356 = 12% e.g. omission error for forest = 43/356 = 12%
The non-diagonal row cells are commission errorsThe non-diagonal row cells are commission errorse.g. commission error for corn 117/459 = 25%e.g. commission error for corn 117/459 = 25%
Classified Reference Data Data Water Sand Forest Urban Corn Hay Row Total Water 480 0 5 0 0 0 485 Sand 0 52 0 20 0 0 72 Forest 0 0 313 40 0 0 353 Urban 0 16 0 126 0 0 142 Corn 0 0 0 38 342 79 459 Hay 0 0 38 24 60 359 481Col Total 480 68 356 248 402 438 1992
Error MatrixError Matrix
correctly classified in each category correctly classified in each category
producer's accuracy = producer's accuracy = ---------------------------------------------- the ---------------------------------------------- the total pixels used in the category (col total) total pixels used in the category (col total)
Omission error = 1 (100%) - producer's accuracy Omission error = 1 (100%) - producer's accuracy
Classified Reference Data Data Water Sand Forest Urban Corn Hay Row Total Water 480 0 5 0 0 0 485 Sand 0 52 0 20 0 0 72 Forest 0 0 313 40 0 0 353 Urban 0 16 0 126 0 0 142 Corn 0 0 0 38 342 79 459 Hay 0 0 38 24 60 359 481Col Total 480 68 356 248 402 438 1992
Error MatrixError Matrix
correctly classified in each category
user's accuracy = ------------------------------------------------------- the total pixels used in the category (row total)
Commission error = 1 (100%) - user's accuracy
Classified Reference Data Data Water Sand Forest Urban Corn Hay Row Total Water 480 0 5 0 0 0 485 Sand 0 52 0 20 0 0 72 Forest 0 0 313 40 0 0 353 Urban 0 16 0 126 0 0 142 Corn 0 0 0 38 342 79 459 Hay 0 0 38 24 60 359 481Col Total 480 68 356 248 402 438 1992
KHAT StatisticsKHAT Statistics ► A measure of the difference between the A measure of the difference between the
actual agreement between reference data actual agreement between reference data and the results of classification, and the and the results of classification, and the chance agreement between the reference chance agreement between the reference data and a random classifierdata and a random classifier
KHAT StatisticsKHAT Statistics ^ observed accuracy - chance
agreement k = -------------------------------------------------- 1 - chance agreement
► The KHAT value usually ranges from 0 to 1
► 0 indicates the classification is not any better than a random assignment of pixels
► 1 indicates that the classification is 100% improvement from random assignment
KHAT StatisticsKHAT Statistics r rr r
N × N × x xiiii - - (x (xi+i+ × x × x+i+i) ) ^ i=1 i=1^ i=1 i=1 k = ----------------------------------- k = ----------------------------------- rr N N22 - - (x (xii++ × x × x+i+i) ) i=1 i=1
r - number of rows in the error matrixr - number of rows in the error matrix
xxiiii - number of obs in row i and column i (the diagonal cells) - number of obs in row i and column i (the diagonal cells)
xxi+i+ - total obs of row i - total obs of row i
xx+i+i - total obs of column i - total obs of column i
N - total of obs in the matrix N - total of obs in the matrix
KHATKHAT
KHAT StatisticsKHAT Statistics ► KHAT considers both omission and KHAT considers both omission and
commission errors commission errors
Readings Readings
► Chapter 7Chapter 7