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

Transcript

1 26 1 V o l. 26 N o Jou rnal of N an jing In stitu te of M eteo ro logy Feb : (2003) ,,, (, ) : GM S25 ( Λm ) ( Λm ), , (radial base function neu ral netw o rk, RBF),, 6 86 % ; 11, 67 % RBF : ; ; : P405; P : A [ NOW CA ST IN G ] [ 2, ] [ 3 ] [ 4 ] [ 5 ] [ 6 ], W elch [ 7 ] 3 (1) (SDA ) ; 2)BP ; 3) (PNN ) ), , (Sequen tial Fo rw ard Selection, SFS) 20, Boo tstrap, %, % % Bankert [ 8 ] PNN 10, ho ld2one2ou t %, boo tstrap %, 5 (,,, ) % M iller [ 9 ] BP 9, 82 % C ity Po lytechn ic L ee [ 10 ] BP GM S : ; : : : (19622 ),,,,,,.

2 , ( ) ( ), 60 %, 70 % NOAA LAND SA T GM S [ ] BP GM S 4 ( M CC),, F isher, F isher, : ;, RBF GM S 11, 1 GM S25 ( Λm ) ( Λm ) km km L am bert Confo rm al Con ic, 110 E, 30 N ( 1), , , :, : (243 ) (618 ) (207 ), 207, 103, 103, 309, RBF, 1 F ig. 1 Samp ling area 1

3 1 : 91 1 T able 1 D efinitions of cloud classes F F F F72 F F F F F F F F 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

4 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), 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

5 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, RBF ( F00, F23, 1) 2 4 T able 2 T esting results fo r 4 classes of clouds F00 F23 F72 F T able 3 T esting results fo r 11 classes of clouds F00 F11 F21 F23 F32 F F42 F51 F72 F81 F T able 4 R ecognition of th in clouds

6 T able 5 R ecognition of convective clouds , 86 %, 100 % 95 % %, 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: [ 2 ],. [J ]., 1994, 17 (1) : [ 3 ],. [J ]., 1995, 18 (4) : [ 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) : [ 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) : [ 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) : [ 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) : [ 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) :

7 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) : [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) : [ 12 ],,,. GM S [J ]., 1998, 9 (4) : [ 13 ]. [M ]. :, [ 14 ] A bh ijit S Pandya. [M ]gg,. :, [ 15 ]. [M ]. :, [ 16 ],,,. M A TLAB [M ]. :, 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 , 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 ( Λm ) and infrared ( 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 km 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