Remote Sensing Image Segmentation Algorithm Based on Multi-agent and Fuzzy Clustering

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1 017 nd International Conferene on Environental Siene and Energy Engineering (ICESEE 017) ISB: Reote Sensing Iage Segentation Algorith Based on Multi-agent and Fuzzy Clustering Lei LIU, Lin-li ZHOU * and Hui-fang BAO Institute of Intelligent Mahines, Chinese Aadey of Sienes, Heifei 30031, China *Corresponding author Keywords: Reote sensing iage, Segentation, Multi-Agent syste, Fuzzy lustering. Abstrat. Reote sensing is a tehnology for onitoring surfae inforation in real tie, reote sensing iage interpretation has iportant applation value in ilitary and ivil fields [1]. In this paper, aiing at the design of reote sensing iage segentation algorith, we propose a reote sensing iage segentation ethod based on ulti-agent syste and fuzzy lustering. The proposed ethod extrats and fuses the intensity, texture and edge inforation in ollaboration and penalization, and it provides theoretal and tehnal support for reote sensing iage segentation. Introdution Reote sensing (RS) is a tehnology whh an be used to obtain the dyna inforation of the earth surfae reotely and without ontat fro the last entury in 60s. On the basis of the further researh on the tehnology of oputer and graphs[], reote sensing tehnology has been developed rapidly, its role in the national eono onstrution and national defense onstrution is beoing ore and ore proinent[3]. The reote sensing tehnology is built on the interation of eletroagnet waves with ground objets, by reeiving and proessing features of eletroagnet waves refleted or radiated bak to obtain ground objet inforation. Satellite reote sensing iage target detetion is a kind of iage interpretation tehnology, whh is based on the features of reote sensing iage, and obined with the iage distribution and haraterists[4]. The type and loation of sensors, weather, wave band and resolution has a great effet on satellite reote sensing iage, therefore, there will be geoetr distortion and noise interferene in reote sensing iage, the design of the algorith with good robustness and adaptability is a key proble in the field of reote sensing iage interpretation[5]. In the field of iage segentation, the effetive applation of target edge and texture inforation an signifantly iprove the auray of segentation results, but extration and fusion of these features are diffult. Iage feature synergist extration and fast parallel ipleentation is a goal of reote sensing iage segentation[6]. Multi-Agent is a kind of tehnology whh an intelligently utilize the ultiple features of the target and realize the feature fusion. It uses ultiple operators to extrat ultiple features in the sae iage[7], then respetively get segentation result of the ultiple features, finally, the fusion riterion is used to get higher auray of segentation. Beause of its intelligene, autonoy and parallelis, the ulti-agent syste has ertain advantages to iprove the reognition auray[8]. Organization of the Text Multi-Agent Syste Agent syste is a kind of intelligent oputing entity that an sense the environent and affet the environent, and has a high degree of autonoy. ulti-agent Syste (MAS) is a distributed solution syste whh is oposed of two or ore Agents through onsultation, ooperation and opetition to realize the oon goal. Single Agent's ability is not enough to solve the whole proble, they 01

2 ust interat with eah other in order to ahieve the purpose of solving probles, planning, searhing, deision-aking and learning. Therefore, the ost iportant feature of the ulti Agent syste is the interation between various Agents. MAS syste works as shown in Fig. 1. Figure 1. MAS syste working ode. In MAS, eah Agent in the proess of ahieving its own goals, not only by the onstraints of the environent, but also by other Agent. This effet ay be diretly aoplished through the interation between Agents, it ay also be ahieved through hanging the environent of the oon Agent. The goal of MAS is to ahieve the goal of a single Agent annot be aoplished through the ooperation between Agents with a oon goal. Design of Segentation Syste Based on Multi-Agent Figure. Struture of segentation syste based on FCM. The design shee of target detetion syste based on fuzzy lustering and ulti-agent is shown in Fig.. It ainly onsists of three parts: the first part is the feature extration layer, whh is oposed of 3 Agent systes, they respetively realize the extration of target intensity, texture and edge feature in reote sensing iages; the seond part is the segentation layer, whh is oposed of 3 Agent based on FCM (Fuzzy C-Means lustering), they respetively ahieve the iage segentation based on intensity, texture and edges; the last part is the fusion layer of segentation results, whh is used to fuse the advantages of different features in the segentation results to ahieve a better segentation auray. Eah Agent odule is based on the reation odel proposed by Brooks, ainly inludes three parts: sensor, deision-aking, ation, under the guidane of the environent these three parts are yl to ahieve, the single Agent odel does not need to use prograing to realize oplex reasoning planning and knowledge representation, whh is easy to ipleent. 0

3 Feature Extration Layer The first part is feature extration layer, whh is used to extrat the intensity, texture and edge feature of reote sensing iage. Where the intensity feature is the gray value of iage pixel, texture feature extration based on the odel of gray level o-ourrene atrix, and edge extration based on Canny operator. Gray level o-ourrene atrix is a oon ethod to desribe texture by studying the spatial orrelation of gray level. First define a diretion and a gray level o-ourrene atrix T ( * ) with pixel as unit step for the iage, where M (I, J) is defined as the frequeny at whh the gray level i and j pixels appear siultaneously at a sae point and the point whh is a step distane along the defined diretion, is the nuber of gray level division. The ontrast, inverse differene oent, entropy and autoorrelation an be defined by gray level o-ourrene atrix, and it an be used to easure the feature of iage texture in brightness hange, hoogeneity and onsisteny. Canny edge detetion algorith based on a ulti-order boundary operator, where the four filters are used to detet the horizontal, vertal and diagonal edges of the iage, the edge detetor returns a value Gx of horizontal oponent and a value Gy of vertal oponent, aording to the edge gradient and diretion, we an deterine the boundary feature of the iage: G G x G y (1) G artan( G y x ) Design of Agent Based on FCM In the split sublayer, 3 FCM-Agent odules need to be designed, whh are used to obtain segentation results based on iage intensity, texture and edge feature respetively. FCM is a kind of lustering algorith based on partition, its idea is that the siilarity between objets divided into the sae luster is the largest, and the siilarity between different lusters is the sallest. The objetive funtion of FCM is: () J ( U, V ) i1 1 u d ( x, v ) Constraint onditions are: i (3) u 1 1 i (4) Fuzzy C-Means lustering algorith is a siple iterative algorith. FCM use the following steps to deterine the luster enter Ci and ebership atrix U of pixel points: Step 1: initialize the ebership atrix U with rando nubers between 0 and 1; Step : alulate luster enters i, i=1,...,c: i1 u i1 u x i Step 3: update degree of ebership: (5) 03

4 u C 1 d( xi, v ) ( ) d( x, v ') 1 1 i (6) Sine FCM onverges to an optial solution is not able to ensure, and the perforane of the algorith depends on the initial lustering enter. Therefore, we an either use another fast algorith to deterine the initial luster enter, or start the algorith with different initial luster enters for eah tie, do several experients. Design of Fusion Layer Agent Fusion layer Agent is used to realize effetive adaptive fusion of segentation results under ultiple features, in order to get better results of the segentation. Fusion layer takes the voting strategy, the fusion proess is shown in Fig. 3. Experiental Results and Analysis Figure 3. Fusion layer agent. This experient ais at the effetive segentation of reote sensing iage, the iage of Munh, Gerany is aptured fro the God eyes. The segentation ethod of reote sensing iage based on ulti-agent and FCM is validated and analyzed by using 7 oputers with G eory. Iage size is 51 * 51 pixels, the resolution is 0.5 eters, we use different gray level display segentation results obtained. (a)original iage Figure 4. Segentation results. (b)segented iage It an be found that the iage is seriously polluted by noise. This ethod an effetively segent ost of the region, the segentation result is oplete, the effet is also good, but the soothness and anti-noise perforane of the segented area need to be further iproved. Suary This paper proposes the ethod of the Reote sensing iage segentation based on a ulti-agent syste and FCM, ulti-level agent is used to realize extration, detetion and fusion of iage 04

5 features, and gets a better segentation results. In addition, the key agent design for the segentation task is realized by the ethod based on fuzzy lustering that uses fuzzy learning and unsupervised lustering to iprove the auray of segentation results and noise iunity. The ulti-agent syste proposed in this paper has ahieved good results in the field of reote sensing iage segentation. Aknowledgeent This work was supported by the ational atural Siene Foundation of China (Grant o ). Referenes [1] I. De, B. Chanda, B. Chattopadhyay. Enhaning effetive depth-of-field by iage fusion using atheatal orphology [J]. Iage and Vision Coputing, 006, 4(1): [] Z. Xi, T. Hesketh. Disrete tie integral sliding ode ontrol for overhead rane with unertainties [J]. IET Control Theory and Applations, 010, 4(10): [3] Weker T.J. Effient iage segentation using partial differential equations and orphology [J]. Pattern-Reognition 001, 34 (9): [4] Anil K. Jain, Robert P.W. Duin, JianChangMao. Statistal Pattern Reognition: A Review [M]. IEEE Transations on Pattern Analysis and Mahine Intelligene, 000. [5] RA. Showengerdt. Reote sensing, third edition: odels and ethods for iage proessing [M], Aade Press, [6] Chang Le. Multi-Agent syste eory evolution exerises based on [D]. Beijing: Tsinghua University, 00. [7] Zhang Yajuan, ulti Agent syste in the reote sensing iage target reognition syste in the applation of researh, developent and applation of oputer, 0 (11): 0-3. [8] H. Larohelleand Y. Bengio, Classifation using disriinative restrted Boltzann ahines, in Pro.5th Int. Conf. Mah. Learn., Helsinki, Finland, Jul. 008, pp