o l. 26 N o Jou rnal of N an jing In stitu te of M eteo ro logy Feb. 2003

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26 1 V o l. 26 N o. 1 2003 2 Jou rnal of N an jing In stitu te of M eteo ro logy Feb. 2003 : 100022022 (2003) 0120089207,,, (, 210044) : GM S25 (10. 5 12. 5 Λm ) (0. 55 0. 9 Λm ), 1999 7 10 57 58 59 12 2 912 8 8, (radial base function neu ral netw o rk, RBF),, 6 86 % ; 11, 67 % RBF : ; ; : P405; P422. 15 : A [ 1 20 70 80 NOW CA ST IN G ] [ 2, ] [ 3 ] [ 4 ] [ 5 ] [ 6 ], 20 90 W elch [ 7 ] 3 (1) (SDA ) ; 2)BP ; 3) (PNN ) ), 5 5 200, (Sequen tial Fo rw ard Selection, SFS) 20, Boo tstrap, 85. 6 %, 87. 6 % 87. 2 % Bankert [ 8 ] PNN 10, ho ld2one2ou t 79. 8 %, boo tstrap 77. 1 %, 5 (,,, ) 91. 2 % M iller [ 9 ] BP 9, 82 % C ity Po lytechn ic L ee [ 10 ] BP GM S : 2001212220; : 2002205215 : : (19622 ),,,,,,.

90 26 5, ( ) ( ), 60 %, 70 % NOAA LAND SA T GM S [ 11212 ] BP GM S 4 ( M CC),, F isher, F isher, : ;, RBF GM S 11, 1 GM S25 (0. 55 0. 9 Λm ) (10. 5 12. 5 Λm ) 13. 22 km 13. 03 km L am bert Confo rm al Con ic, 110 E, 30 N 57 58 59 ( 1), 1999 7 10 8 8, 12 2 912, :, : (243 ) (618 ) (207 ), 207, 103, 103, 309, RBF, 1 F ig. 1 Samp ling area 1

1 : 91 1 T able 1 D efinitions of cloud classes F00 243 F21 202 F32 110 F72 F91 289 183 F23 207 F11 618 F42 231 F51 459 F61 111 F81 213 F41 46 2 3 Fou rier ( ) a ( ta, Σ= a t) ( Ξa, Σ= Ξga, t Ξ ) a, Ωa, Σ ta, Σ, Ξa, Σ Ξa, Σ= Ξ0ga ( ) ;, a, Ωa, Σ ta, Σ, Ξa, Σ,,, (Sho rt T im e Fou rier T ran sfo rm, ST FT ) Υ(t) Ω( t) {u n (t) ; n Z } ( ),,,,, 2 3, S A A A 3+ DA A 3+ A DA 3+ DDA 3+ A A D 3+ DA D 3+ A DD 3+ DDD 3,, A, D, ( ) : 1 :, 2 16, 3, (i, j ) i j,, (0, 0) S, (1, 0) 1 X 10, (1, 1) 1 X 11, (2, 0) 2 0

92 26 2 1 3 F ig. 2 12D 32level w avelet packet decompo sition tree 2 : S 20 X 20, S 21 X 21, S = 15 S 2j (1) j= 0 3 : S 2j ( j = 0, 1,, 15) E 2j ( j = 0, 1,, 15), E 2j = gs (t) g 2 2j dt = n k= 1 gx jkg 2 (2), x jk (j = 0, 1,, 15, k= 1, 2,, n, n ) S 2j 4 :, T, E = 15 j= 0 ge 2jg 2 1g2 T = {E 2jgE, j = 0, 1,, 15} (3), 3 3 2 2 F ig. 3 22D 22level w avelet packet decompo sition tree fo r satelitte im ages o r cloud samp les 3 (RBF) rad bas (n) = e - n2, n

1 : 93, 4 : S 1 S 2 RBF [ 13 ], K 2 4 [ 14 ] [ 13 F ig. 4 P lo t of radial base ] neu ron tran sfer function, [ 15 LM S ] [ 16 ] :,,, 1, 0 4 2 5 RBF ( F00, F23, 1) 2 4 T able 2 T esting results fo r 4 classes of clouds F00 F23 F72 F91 1. 00 0. 75 0. 75 0. 95 0. 86 3 11 T able 3 T esting results fo r 11 classes of clouds F00 F11 F21 F23 F32 F41 0. 88 0. 64 0. 61 0. 66 0. 60 0. 44 F42 F51 F72 F81 F91 0. 75 0. 60 0. 55 0. 77 0. 92 0. 67 4 T able 4 R ecognition of th in clouds 0. 90 0. 50 0. 55 0. 65

94 26 5 T able 5 R ecognition of convective clouds 0. 90 0. 95 0. 925 2, 86 %, 100 % 95 % 3 11 67 %, 88 % 92 %,, 44 %,,,, 4,,.,,,, 5,,,, : [ 1 ] L iljas E. A utom ated T echniques fo r the A nalysis of Satellite C loud Im agery [M ]ggn ow casting K A. B row ning: A cadem ic P ress, 1982: 1772190. [ 2 ],. [J ]., 1994, 17 (1) : 1172124. [ 3 ],. [J ]., 1995, 18 (4) : 5302535. [ 4 ] L ee J, W eger R C, Sengup ta S K, et al. A neural netwo rk app roach to cloud classification [J ]. IEEE T rans Geo sci Remo te Sens, 1990, 28 (5) : 8462855. [ 5 ] Baum B A, V asanth Tovinkere, T ay T itlow, et al. A utom ated cloud classification of global AV HRR data using a Fuzzy logic app roach [J ]. J A pp M et, 1997, 36 (11) : 1 51921 535. [ 6 ] Peak J E, T ag P M. Segm entation of satellite im agery using h ierarch ical th resho lding and neural netwo rk s[j ]. J A pp M et, 1994, 33 (5) : 6052616. [ 7 ] W elch R M, Sengup ta S K, Go roch A K. Po lar cloud and surface classification using AV HRR im agery: an intercomparison of m ethods[j ]. J A pp M et, 1992, 31 (5) : 4052420. [ 8 ] Bankert R L. C loud classification of AV HRR im agery in M aritim e regions using a p robabilistic neural netwo rk [J ]. J A pp lm et, 1994, 33 (8) : 9092918.

1 : 95 [ 9 ] M iller S W, Em ery W J. A n autom ated neural netwo rk cloud classifier fo r use over land and ocean surfaces[j ]. J A pp M et, 1997, 36 (10) : 1 34621 362. [10 ] L ee V icto r C S, H ung S L, Cheng A ndrew Y S, et al. A n Operational C loud C lassifier fo r Satellite Im ages [R ]. P roceedings of the 2ND International Conference on East A sia and W estern Pacific M eteo ro logy and C lim ate, 7210, Sep tem ber, 1992, Hongkong. [ 11 ]. NW Pg + [J ]., 1999, 25 (6) : 27230. [ 12 ],,,. GM S [J ]., 1998, 9 (4) : 4022 409. [ 13 ]. [M ]. :, 1996. [ 14 ] A bh ijit S Pandya. [M ]gg,. :, 1999. [ 15 ]. [M ]. :, 1999. [ 16 ],,,. M A TLAB [M ]. :, 2000. A Neural Network Approach to the Automated Cloud Classif ication of GM S Imagery over South-East Ch ina M ar itim e Reg ion s J IAN G D e2m ing, CH EN W ei2m in, FU B ing2shan, W AN G J ian2kai (D epartm ent of E lectronic Engineering, N IM, N anjing 210044, Ch ina) Abstract: T h is paper p resen ts an au tom ated and efficien t cloud classification schem e based on radial base function neu ral netw o rk (RBF), w h ich has an average classification accu racy of m o re than 86 % fo r less than 5 cloud pattern s and 67 % fo r m o re than 10 cloud pattern ṡ A n additional self2o rgan ized com petitive neu ral netw o rk is also suggested to find ou t the cen ter of the h idden layer neu ron s of RBF netw o rk, w h ich greatly am elio rates the efficiency of the RBF classifieṙ Featu res ab stracted by u sing the tw o2dim en sional w avelet packet 32level decom po sition p rovide essen tials fo r the descrip tion of cloud pattern s, thu s im p roving the capab ility of pattern recogn ition of neu ral netw o rk classifieṙ T he input dataset em p loyed in the schem e are defined by 2912 sam p les of 8 8 p ixels size taken in Ju ly2o ctober 1999 over SE Ch ina m aritim e region s from the visib le (0. 55 0. 9 Λm ) and infrared (10. 5 12. 5 Λm ) channels of Geo stationary M eteo ro logical Satellite 5 (GM S25). T hese im ages in L am bert Confo rm al Con ic P ro jection and in a coarsened reso lu tion of 13. 03 km 13. 22 km fo r bo th channels are dow n loaded from the m eteo ro logical operational netw o rk ṡ Each of the sam p les in the dataset is classified in to one of the eleven p redefined classes in acco rd w ith the SYNO P codes u sed in w eather repo rtṡ Key words: cloud classification; neu ral netw o rk; satellite im agery