Airplane Detection in Remote-Sensing Image. with A Circle-Frequency Filter

Transcription

1 Arplane Detecton n Remote-Sensng Image wth A Crcle-Frequency Flter CAI Hongpng, SU Y (School of Electronc Scence and Engneerng, atonal Unversty of Defense Technology, Changsha, Chna) ABSTRACT Ths paper presents a new approach to detect arplanes n panchromatc remote-sensng mages. A crcle-frequency flter(cf- flter) s gven to locate arplane centers from the bacground. The flter extracts canddate ponts of arplane centers frst. Then through a smple clusterng method, arplane centers can be located. 8 panchromatc mages of 1.0m~4.0m resoluton, ncludng 65 arplanes, are tested wth ths approach. 59 arplanes are detected and 5 tmes are false alarmed. Index Terms--Crcle-frequency flter, Arplane detecton, Remote-sensng mage processng, Fourer transform, Panchromatc mage. 1. ITRODUCTIO In mage processng, target detecton methods can be classfed nto two classes. One s mage segmentaton or edge detecton [3,4] based. These methods can be appled to almost all targets But complex post-processng s usually needed and the performance s subject to the data qualty. The other s based on a flter or a transform. Many researchers have tred to fnd a sutable flter or transform for a certan target. These methods use flters or transforms to dstngush targets from the bacground. They perform well because they have taen advantage of target features. There were researches on face detecton [1,2,5], road-sgn detecton [6] and so on. The ey ssue of these methods s to desgn a sutable flter or transform for a certan target. The presented approach n ths paper belongs to the latter class n whch a crcle-frequency flter(cf-flter) s desgned for detectng arplanes. Ths dea can gve a clue to other objects. There are lots of necesstes for arplane detecton and recognton from panchromatc automatcally or sem-automatcally [3,7,8,9,10]. Most of the researches focus on how to recognze the types of arplanes on the premse that arplanes have been extracted [7,8,9,10], whle how to detect arplanes has fewer researchers. The man reason s that many factors, such as small sze, shadow and complex bacground, mae arplane detecton dffcult and more ntractable than arplane recognton. The exsted research on arplane detecton uses template matchng based on mage segmentaton or edge detecton. Arplanes usually become several segments by mage segmentaton and t s dffcult to get a close outlne of an arplane. So the remedy s segment combnaton or edge lnng. These methods are complex and subject to the data quanlty. In Igor s research [3], the result was 8 correct matches, 29 mssed matches and 8 false postves, whch obvously has far dstance from satsfacton. The approach n ths paper s nspred by the face detecton method n S.Kawato s research [1,2] n whch a crcle-frequency flter(cf-flter) s used to dentfy Between-the-Eyes. Our approach needn t edge detecton or mage

2 segmentaton, but flter the whole mage wth the desgned CF-flter to get a new fltered mage. Because arplane centers have hgher response whle bacground has lower response to CF-flter, we select the ponts wth bgger outputs as arplane-center canddates. Fnally, arplane centers can be located by a smple clusterng method. 2. CIRCLE-FREQUECY FILTER Snce there are many types of arplanes whch have dfferent shapes, t s not an easy tas to detect all of them or most of them. In panchromatc remote-sensng mages, most of arplanes share the same features as follows: Frst, most of arplanes are brghter than bacground; Second, most of them have four man bulges head, left wng, tal, rght wng. Fnally, ther szes are n a certan range. Through analyss above, f a crcle of a certan radus s centered at the arplane center, the ntenstes along the crcle trend to be brght-dar-brght-dar-brght-dar-brght-dar. Fgure 1(b) plots the 40 pxel values along the crcle n the Fgure 1(a) whch starts at the top of the crcle and goes counter-clocwse. In Fgure 1(b), the curve has 4 valleys and 4 peas whch respectvely correspond to the bacground and bulges of the arplane. Whle when the crcle goes away from the arplane, the four-cycle pattern trends to valsh. The crcle-frequency flter defned below s such a flter for dentfyng whether the curve has 4 peas and 4 valleys. (a) Fgure 1. A plot of 40 pxel values along the crcle centered at the arplane center (b) Let f ( = 0,1,..., 1) denote pxel values along a crcle of radus r centered at (,. The CF-flter calculates the magntude f (, of the seres f as follows: f (, = ( f cos = 0 1 π 2 8π 2 ) + ( f sn ) (1) = 0 Where f goes from the top (, j r ) along the crcle n the counter-clocwse drecton. In essence, ths flter s the magntude of a dscrete Fourer transform of seres f ( 4 cycles are used ). Snce the ntenstes along the crcle centered at arplane centers resemble 4 cycles of sne or cosne functon, whch s shown n Fgure 1(b), f the CF-flter s appled to the whole mage, arplane centers wll appear brght whle bacground wll appear dmmng. We verfy ths concluson through experments. Fgure 2 shows CF-fltered mage of Fgure 1(a). The magntudes are normalzed to the gray levels from 0 to 255 for beng dsplayed as an mage. We can see the CF-fltered output at the arplane center s much hgher than those at other places. Fgure 3(b) s the CF-fltered output of CF-fltered mage of Fgure 3(a) whch

3 contans 12 arplanes. After beng fltered by CF-flter, the complcated bacground appears dmmng, whle the arplane centers become brght, whch proves that CF-flter can dentfy arplane centers from the bacground. Fgure 2. CF-fltered mage of Fgure 1(a) For the well-nown Fourer transform characterstc, the ntensty output of CF-flter does not change when an mage rotates n the mage plane,.e. the CF-flter has rotaton nvarance. So the presented approach has no need to tae every drecton of arplanes nto account as the tradtonal mage matchng methods [3,9]. 3. DETECTIO APPROACH USIG CIRCLE-FREQUCY FILTER As has been descrbed above, the complcated bacground appears dmmng through CF-flter, whle the arplane centers appear brght. We selected the ponts wth hgher outputs of CF-fltered mage as canddate ponts of arplane centers. Assume M s the maxmum of magntudes f (, ( = 0,1,... m, j = 0,1,..., n ), we tae α M ( 0 < α < 1) as the threshold. The ponts whose outputs are hgher than α M are consdered as arplane-center canddates whch are denoted by the set E = {(, f (, > αm}. Red crcles are used n Fgure 3(c) to sgn all the canddates of arplane centers of Image Bloc01 ( α = 0. 7 ). From ths fgure, we can see that the hgher magntudes correspond to ponts near the arplane centers, whch means that the CF-flter has extracted the essental features of arplanes. Snce the canddates cluster round each arplane centers and every cluster s far form each other, we can use a clusterng method to classfy them easly [11].If there are K classes C 1, C2,..., C K got after clusterng, whch mples that there are K arplanes detected. After clusterng, the class centers ( ( ( ), y( )) x, 1,2,..., K = )are consdered as the arplane centers whch are calculated as follows: x 1 ( ) = x, y ) = = 1 1 ( y (2) = 1 where denotes the sample num of -th class, x and y denote the x-coordnate and y-coordnate of the -th sample of -th class respectvely. Fgure 3 s the routne of the presented arplane detecton approach usng crcle-frequency flter. Fgure 3(b) s CF-fltered mage n whch the arplane centers are brght and the bacground appears dmmng. Fgure 3(c) denotes the arplane-center canddates by red crcles. We can see that there are almost a few crcles near each arplane center. To locate the centers exactly, we calculate the class centers as arplane centers through clusterng. The result s shown n Fgure 3(d). There are 12 arplanes n Image Bloc arplanes are detected by the presented approach whle 2 small arplanes mssed.

4 (a) (b) (c) (d) Fgure 3. The presented arplane detecton approach usng the crcle-frequency flter (a) Orgnal mage(image Bloc01, ) (b) Gray mage of the output of CF-flter ( r = 6, = 40 ) (c)arplane-center canddates( α = 0. 7 ) (d)arplane centers after clusterng 4. EXPERIMETAL RESULTS In ths paper, 8 panchromatc satelltc mages of 1.0m~4.0m resoluton, ncludng 65 arplanes are tested wth the presented approach. 59 arplanes are detected and 5 tmes are false alarmed. The results have been shown n Fgure 4. The frst three mages have dstnctve arplane shapes and have sharper contrasts between arplanes and bacground, so we get satsfactory results of only 3 mssed arplanes and no false alarms. The reason for mssed detecton s that the radus r sn t sutable for the 3 small arplanes n CF-flterng. Image Bloc04 Bloc05 and Bloc08 have clear shadows and the bacground around arplanes are comparatvely brghter, whch maes the presented approach perform worse than the frst three mages. In spte of the dsadvantage, most of arplanes are detected wth few false alarms. In concluson, the experments demonstrate that the presented approach usng CF-flter s effectve n arplane detecton. (a) (b)

5 (c) (d) (e) (f) (g) (h) Fgure 4. The results of our presented approach to detect arplanes (a)(b)(c)(d)(e)(f)(g)(h) s the result mages of Image Bloc01, Bloc02, Bloc03, Bloc04, Bloc05, Bloc06, Bloc07, Bloc08 5. COCLUTIO The crtcal characterstc of the approach s that a crcle-frequency flter s desgned as a means of dentfyng arplanes from the bacground. Both the theory analyss and the experments show CF-flter vald n arplane center locatng. One of ey ssues n the approach s how to choose an approprate radus r adaptvely. Another ssue s how to detect all arplanes of dfferent szes n an mage, whch s also related wth radus choosng. So, we ntend to explore these drectons above n our ongong wor.

6 References [1] S.Kawato and.tetsutan, Crcle-frequency flter and ts applcaton, Int. Worshop and Andvanced Image Technology, pp , 8-9 February 2001, Taejon, Korea. [2] S.Kawato and J.Ohya, Two-step approach for real-tme eye tracng wth a new flterng technque, Pro. Int. Conf. On System, Man & Cybernetcs, pp ,2000. [3] I. Ternovsy, D.aazawa, S. Campbell and R. E. Sur, Bologcally nspred algorthms for object recognton, Inter. Conf. Integraton of Knowledge Intensve Mult-Agent Systems: KIMAS'03, Boston, Massachusetts, September 30 - October 3, [4] A. Ylmaz, X. L and M. Shah, Contour-based object tracng wth occluson handng n vdeo acqured usng moble cameras, IEEE Trans. Pattern Analyss and Machne Intellgence, 2004, 26(11): [5] G. Loy and A. Zelnsy, Fast radal symmetry for detectng ponts of nterest, IEEE Trans. Pattern analyss and Machne Intellgence, 2003, 25(8): [6] G. Loy and. Barnes, Fast shape-based road sgn detecton for a drver assstance system, Proc of Inter. Conf. on Intellgent Robots and Systems (IROS), [7] Wu Hao. Research on technques for arplane recognton n remote-sensng mages [master thess]. Changsha: School of Electronc Scence and Engneerng, atonal Unversty of Defense Technology, 2000 (n Chnese). [8] Ma Shpng, B Duyan and Chen Lanlan, Arplane recognton based on mage matchng technology, 2004, 30(5): (n Chnese). [9] L Yngchun, Chen Hexng, Yang Janbo and Ln Ln, Algorthm of aeral flms recognton based on moment nvarant, 2002, 20(3): (n Chnese). [10]Cheng Yongme, Pan Quan, Zhang Hongca and Wang Gang. Computer Intellgent mage recognton algorthm. 2004, 24(2): (n Chnese). [11]Ban Zhaoq and Xuegong, Pattern Recognton. Press of tsnghua unversty, Bejng, 2000 (n Chnese).