A New Approach to Accuracy A ssessm en t of C la ssif ica tion s of Rem otely Sen sed Da ta

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1 JOURNAL OF REMOTE SENSING Vol. 10, No. 1 Jan., 2006 : (2006) , 2, 1, 3, 1 (1., ; 2., ; 3., ) :,,,,,,,,,,,, :, : ;; : TP : A A New Approach to Accuracy A ssessm en t of C la ssif ica tion s of Rem otely Sen sed Da ta ZHENG M ing2guo 1, 2, CA I Q iang2guo 1, Q IN M ing2zhou 3, YUE Tian2xiang 1 (1. Institute of Geographic Sciences and N atural Resources R esearch, CAS, B eijing100101, China; 2. Graduate School of the Chinese A cadem y of Sciences, B eijing100039, China; 3. College of Environm ent and Planning, Henan U niversity, Henan Kaifeng475001, China) Abstract: Accuracy assessment is an indispensable step in the p rocess of classification of remotely sensed data. : ;: : ( ), (CXIOG2A04210) : (1971),,, The common method is carried out through confusion matrix established on reference data, which has three deficiencies: the heavy workload, inability to guarantee the comp lete correctness of reference data, the cost of reduction error resulting in the increase of workload. In remotely sensed imagery, the feature vector belonging to one category obeys the normal distribution. Based on this hypothesis and statistic theory, a new method is p roposed established on category distribution. The reference data is unnecessary for p roposed method. For the supervised classification, the workload is extremely little. The key to the p roposed method is that the category population can pass the hypothesis test of a certain distribution, in this case, p roducer s accuracy can be figured out easily. Given the number of the category population, the user s accuracy can be figured out too, and then the overall accuracy can be estimated by user s accuracy and area p roportions of all categories after classification. Finally, the p roposed method in this paper was app lied to image classification for Zhengzhou city as an examp le. The result shows: if the distribution of category population can be given, p roducer s accuracy obtained by common method and p roposed method comp letely conform s in the perspective of statistics. Keywords: accuracy assessment; remote sensing classification; hypothesis testing

2 ,,, [ 1 ] [ 2 ],,, [ 3 ],,, ( Confusion matrix) [ 4 ] (Overall accuracy) Kappa, ( Producer s accuracy) (U ser s accuracy) [ 5 ], [ 6 ], [ 4 ],:, [ 7 ] [ 8, 9 ], [ 1012 ],,,,,, 100%,,, [ 2 ], 100% [ 4 ],,,,, ( ), [ 4 ], [ 3 ] ;, [ 6 ],,,,ERDAS IMAGINE, ,, [ 13, 14 ],:,( Spectral class) uu u,,,, [ 15 ],,, u,,,, (Maximum likelihood classification ) 100%,,,,, 1 1 L i,l,a 1, a 2,, a n, ( a 1 ), ( a 2 ),, ( a n ) i,1 i L, X, X,,,,X,, L i (S ) ;L

3 1 : S 1 = S p 1 + S p S p n, i 1 Fig. 1The real histogram of category population and the category histogram after classification i i,; (S 1 ) i i, ;L (S 2 ) i S i ( pa),( ua) : pa = S 1 /S (1) ua = S 1 / S (2),S, S 1, S 2, S S 1 S i, i ( a 1, a 2 ), ( a 2, a 3 ),, ( a n, a n + 1 ),a 1, a 2,, a n S, S i ( a i, a i + 1 ) p i, i : pa i = p 1 + p p n (3) n, r 1, r 2,, r n, ua 1, ua 2,, ua n, : = r 1 ua 1 + r 2 ua r n ua n (4) : ua i = S 1 /S = S pa i /S (5) S,: i, X i p x,x N X, X i, i : S = N X / p x (6), N X i,(6)s, i i p, N, i : S = N / p (7), N,, N, (7) S 2. 2, 2,,,, ;, , 1100km 2, Landsat25 TM, ,,,,,,

4 Fig. 2Flowchart of accuracy assessment by the method p roposed in this paper % [ 16 ],,, 1998, 4, 5, 3, ( Tasselled cap) ( Principal component),,1 2,,,,, 4, 5, 3,( )( ) ( )121 (, ) 2 (, )3 () /// Erdas AO I(A rea of interest)(region grow AO I),,, Erdas( Image alarm ),,,, 1,, [ 12, 13 ] 7, ,,,1, 2, 7 1, 2, 7,,3

5 1 : 43 1 Table 1The num bers of p ixels and polygon s in tra in ing area s / / / Fig. 3Pixls assigned to high density in three classification results,,,, ,, 2

6 (1, 2, 7) Table 2The process and result of hypothesis testing of popula tion d istr ibution ( band 1, 2, 7) m i p i np i (m i - np i ) 2 / np i = 3119,, 2 161, 2 < ( 15 ) ( ( 15 ) = 3218), = 42184, 9 (10), 2 > ( 9 ) ( ( 9 ) = ), 2 = 96167, 20 (21 ), 2 > ( 20 ) ( ( 20) = ), : m i i ( ) p i i, n4646 (1), np i 2( ),5, 2

7 1 : 45 2, [ 15 ] : F ( X ),, 2 =(m i - np i ) 2 / np i (8) 2 (Chi2Square) m i i, p i F (X ) i, n ,21735,, , ,, , , m i np i,,, ,, (3) 3,,2 3 Table 3Fea ture vector and num bers of p ixels cla ssif ied in to h igh den sity urban area , p x 2p i,, , 1 (2) 7, 2p i 1, p x 3,3, 4 4,,,, 1 7,1,, ( Spectral class) ( Practical class), (6),N X

8 Table 4Cacula tion of the num ber of ca tegory popula tion for h igh den sity urban area ,4,,, 3,,,, a,x, n (x ), n, a ,,,,,3, 7,1,2 1, 2x 1, x 2, x , , , , ( 3), ,, 1, : , , ,,,,, [ 17 ] : U = ( p - P 0 ) / [ P 0 (1 - P 0 ) / n ] 1 /2 (9) U n, p, P 0, 5, 572 (5 ), : [ 18 ], [ 19 ],,,,,,,

9 1 : 47 5 Table 5The cla ssif ica tion result of test area s / / / :42 1, , ,: , ? (9) : U = [ ( ) /572 ] 1 /2 = , a , U < 1196, U,,,, 2 U = 21502, a , U = ,, (1),, ( ),, 1,2,7 12,,,( ), : (1); (2), ; (3) TM,,,, (2) :,,,,,,,,,, (3),,,,,,,, [ 4 ],,,,, (4),,

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