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Remote Sensing Classification Accuracy. 1. Select Test Areas. Selecte test areas in an image to evaluate the accuracy of a classification Test areas should be representative categorically and geographically Sampling methods: uniform wall-to-wall, random, stratified random sampling

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Remote Sensing Classification Accuracy

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Remote sensing classification accuracy l.jpg

Remote SensingClassification Accuracy


1 select test areas l.jpg

1. Select Test Areas

  • Selecte test areas in an image to evaluate the accuracy of a classification

  • Test areas should be representative categorically and geographically

  • Sampling methods: uniform wall-to-wall, random, stratified random sampling

  • Sample size: 50 - 100 pixels each category


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http://aria.arizona.edu/slg/Vandriel.ppt


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2. Error Assessment

  • A classification is not complete until its accuracy is assessed

  • Error matrix

  • KHAT statistics


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Error Matrix

  • Also called confusion matrix and contingency table

  • Compares the ground truth and the results of the classification for the test areas

  • Can be used to evaluate the result of classifying the training set pixels and the results of classifying the actual full-scene


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ClassifiedReference DataDataWater  Sand  Forest   Urban    Corn Hay Row Total

Water480 0 5 0 0 0 485

Sand 0 52 0 20 0 0 72

Forest        0 0 313  40 0 0 353Urban 0 16 0 126 0 0 142

Corn 0 0 0 38 342 79 459

Hay 0 0 38 24 60 359481

Col Total  480       68     356 248 402 4381992

Error Matrix

Diagonal cells are correctly classified pixels

                            correctly classified pixels 1672

Overall accuracy =  ------------------------------- = ------- = 84%                               total pixels evaluated 1992


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ClassifiedReference DataDataWater  Sand  Forest   Urban    Corn Hay Row Total

Water480 0 5 0 0 0 485

Sand 0 52 0 20 0 0 72

Forest        0 0 313  40 0 0 353Urban 0 16 0 126 0 0 142

Corn 0 0 0 38 342 79 459

Hay 0 0 38 24 60 359481

Col Total  480       68     356 248 402 4381992

Error Matrix

In this case, the non-diagonal column cells are omission errors

e.g. omission error for forest = 43/356 = 12%

The non-diagonal row cells are commission errors

e.g. commission error for corn 117/459 = 25%


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ClassifiedReference DataDataWater  Sand  Forest   Urban    Corn Hay Row Total

Water480 0 5 0 0 0 485

Sand 0 52 0 20 0 0 72

Forest        0 0 313  40 0 0 353Urban 0 16 0 126 0 0 142

Corn 0 0 0 38 342 79 459

Hay 0 0 38 24 60 359481

Col Total  480       68     356 248 402 4381992

Error Matrix

correctly classified in each category producer's accuracy =  ----------------------------------------------                           the total pixels used in the category (col total)

Omission error = 1 (100%) - producer's accuracy


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ClassifiedReference DataDataWater  Sand  Forest   Urban    Corn Hay Row Total

Water480 0 5 0 0 0 485

Sand 0 52 0 20 0 0 72

Forest        0 0 313  40 0 0 353Urban 0 16 0 126 0 0 142

Corn 0 0 0 38 342 79 459

Hay 0 0 38 24 60 359481

Col Total  480       68     356 248 402 4381992

Error 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


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KHAT Statistics

  • A measure of the difference between the actual agreement between reference data and the results of classification, and the chance agreement between the reference data and a random classifier


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KHAT 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


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KHAT Statistics

r          r       N × S xii -  S (xi+  ×  x+i) ^         i=1       i=1k = ----------------------------------- r           N2  -  S (xi+  ×  x+i) i=1

r - number of rows in the error matrix

xii - number of obs in row i and column i (the diagonal cells)

xi+ - total obs of row i

x+i - total obs of column i

N - total of obs in the matrix


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KHAT


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KHAT Statistics

  • KHAT considers both omission and commission errors


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Readings

  • Chapter 7


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