The Study of Land Use Classification Based on SPOT6 High Resolution Data

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1 The Study of Land Use Classfcaton Based on SPOT6 Hgh Resoluton Data Wu Song 1, Jang Qgang 1 College of Earth Scences, Jln Unversty, Changchun, Chna College of Geo-Exploraton Scence and Technology, Jln Unversty, Changchun, Chna Abstract. A method s carred out to quck classfcaton extract of the type of land use n agrcultural areas, whch s based on the spot6 hgh resoluton remote sensng classfcaton data and used of the good nonlnear classfcaton ablty of support vector machne. The results show that the spot6 hgh resoluton remote sensng classfcaton data can realze land classfcaton effcently, the overall classfcaton accuracy reached 88.79% and Kappa factor s whch means that the classfcaton result of support vector machne s deal and better than other tradtonal mage classfcaton method. So, the method whch s used hgh-resoluton satellte provde a rapd and feasble way for classfcaton of land use types. 1 Introducton Research of land use classfcaton provdes the basc work for techncal support, such as land plannng and management, land change mechansm analyss and envronmental protecton. Remote sensng technology has become the most effectve means for the acquston of land use nformaton as the technology has many characterstcs, such as macroscopc, dynamc and rapd.at the same tme, usng satellte remote sensng data for automatc classfcaton of land use and thematc nformaton extracton has been the forefront drecton of remote sensng technology applcaton [1], []. So many scholars at home and abroad carred out the research about ths, and the support vector machne (SVM) technology has been wdely used n the automatc classfcaton of land use wth ts characterstcs of small sample tranng, support hgh dmensonal feature space and fast convergence. Wth the rapd development of hgh resoluton remote sensng technology, the use of hgh resoluton remote sensng data at home and abroad for the research of automatc classfcaton of land s ncreasng, and has already obtaned rch success [3], [6]. SPOT satellte s wde band, hgh spatal resoluton remote sensng satellte and has been appled n many ndustres due to ts good applcaton performance, but ts applcaton s less appled for the automatc classfcaton and nformaton extracton of land use. In ths paper, the hgh resoluton remote sensng mage and mage automatc classfcaton technology has combned organcally [7], [8]. Analyss of the spectral characterstcs and hgh resoluton of SPOT6 satellte based on support vector machne (SVM) classfcaton prncple whch realze land nformaton rapd extract classfcaton, that provde evdence for montorng land use stuaton, formulate comprehensve control measures and use polcy [9]. Introducton n the study area and SPOT6 data preprocess.1 The geographcal stuaton n the study area and the selecton of the test data The study area s located n the mddle of Morocco, ths experment selected SPOT6 satellte mages n May 3, 013 as the remote sensng data. The mage range from 30 9 '34 "N ~ 30 33' 56" N to '59 "W ~ 09 W 05' 07" and we choose a typcal area n ths area. The total of land use category n the area s 5,ts manly has plough, forest land, constructon land, water and other classes, so t can test the classfcaton method effectvely as shown n Fg. 1. Fgure 1. SPOT RS mages of study area. The Authors, publshed by EDP Scences. Ths s an open access artcle dstrbuted under the terms of the Creatve Commons Attrbuton Lcense 4.0 (

2 . SPOT6 hgh resoluton data preprocess Spot 6 earth observaton satellte was produced by the European space technology company n September 9, 01, and t was lunched by the PSLV carrer rocket n Inda successfully. In th September, SPOT6 entered the orbt that n the same orbt plane wth Pleades 1A satellte whch was 695 Km hgh, after January 013, the Satellte was nto the formal busness operaton. The satellte can obtan mage data of the spatal resoluton of panchromatc 1.5M and mult-spectrum 6M, and t can receve 600 square klometers mage a day. The servce objects manly dstrbuted n ecologcal envronmental, geology and mneral resources, agrculture, forestry, envronmental protecton and dsaster montorng, telecom network plannng, surveyng and mappng, cty plannng and natonal defense [10], [11]. Bands Table 1. Satellte parameters of SPOT6. Spectral range (μm) Resoluton (m) ~ ~ ~ ~ ~ Imagng Swath (km).3 Remote sensng data preprocess.3.1 Band synthetc Angle of ncdence (º) 60 ±30 When usng remote sensng technology to extract nformaton, t s necessary to study the spectral characterstcs whch wll be benefcal for the combnaton of band that dentfy the target. In ths test, frst of all, n the expermental zone, the spectral characterstcs of each band of mult-spectral data wll be analyzed,then calculate the mean, standard devaton and nformaton entropy of the mage grey value respectvely, add up and compare the nformaton reflected by the band, as t s shown n Table. From the statstcal analyss of the results,t can be seen that the statstcal ndcators of the fourth band s greater than the other bands, whch means that the nformaton concluded n the fourth band s the greatest, and the fourth band (near nfrared) has a sgnfcant role n the vegetaton type classfcaton, n the same tme t has mnmum correlaton wth other bands, so through combnng the bands together based on the forth band whch s fxed on the green channel, we get expermental comparson results, then ntegrate wth expermental zone concluded wth the class stuaton, select 431 band as the band combnaton (fgure 1a), the feature dfference of ths combnaton s bg and t contans rch amount of nformaton whch s benefcal for vsual dscrmnaton and the study of classfcaton automatcally. Table. Basc statstcal nformaton of multspectral mages Basctats M Max Mean Stdev Egenvalue Band Band Band Band Image fuson processng Image fuson technology can make use of the dfferent characterstcs of the data n a maxmze way n order to mprove the vsual effect of mage and the ablty of mage feature recognton and make the mage has a hgher spectral and spatal resoluton n the same tme[1]. Make the panchromatc and mult-spectral data fuson processng can let the SPOT6 data play a role n a maxmum way, there are many algorthms for remote sensng data fuson. Prncpal component analyss (pca), IHS transform method and wavelet transform method were selected for fuson of resoluton n ths text. And the nformaton after the process of fuson was calculated (table 3). From table 3 we can see that 3 knds of fuson method have ther advantages and dsadvantages respectvely, but wavelet transform method s superor to other fuson methods n spectral nformaton keepng and peak sgnal to nose rato. Therefore, the method of wavelet transform was adopted to process mage fuson n ths text based on the am of mage automatc classfcaton. PCA IHS WAVELET Band Mean Table 3. Satellte parameters of SPOT6. Standard devaton Snr Entropy meangradent R G B R G B R G B

3 3 Image classfcaton prncple based on support vector machne Support vector machne (SVM) classfcaton algorthm s a knd of machne learnng algorthms based on statstcal learnng theory, whch s used structural rsk mnmzaton prncple of SRM through solvng quadratc programmng problem under the nequalty constrants [13]-[15]. Structural classfcaton hyperplane n the tranng set T=,(, ) -11=1...n. Assumng that the classfcaton of equaton s <X >+b=0, the equaton should be satsfed wth type (1): <X >+b-1 0 (1) The class nterval accordng to analytc geometry s D=/. The problem can be converted nto that ntroduce Lagrange functon whch s used for solvng ths optmzaton problem n order to make the functon ( ) mnmum[16~17]. () the >0 s the Lagrange multpler, the soluton of the problem must be satsfed type (3) accordng to the KKT condtons: sparseness, so the classfcaton speed of SVM s better than others[18-1]. The essence of SVM: frst of all, to transform the nput space nto a hgher dmensonal space through the nonlnear transformaton whch s defned by the approprate product functon, then to acheve the optonal lnear classfcaton surface by lnear regresson n the hgher dmensonal space. 4 The process of SVM mage classfcaton 4.1 Remote sensng data preprocess Support vector machne (SVM) classfcaton algorthm s a knd of machne learnng algorthms based on statstcal learnng theory, whch s used structural rsk mnmzaton prncple of SRM through solvng quadratc programmng problem under the nequalty constrants[13-15]. Frst of all, feature extracton for hgh resoluton data, then selectng the features as extracton algorthm, such as the meanthe standard devaton and k-l transform. Selected the nterested area of the mage whch the tranng and choce of the sze of texture wndow s the result of test for many tmes. If the sze s too small that wll make not only the tranng speed reduced strongly, but also the result has no sgnfcant change; oppostely, f the sze s so bg that the precson wll be low [9]. As t s shown n t Fg.. {[ X b] Y 1} 0 (3) Therefore, the resultng dscrmnant functon (4) f ( X ) sgn{ n 1 Y X X b} (4) In general case, most of are 0, the others are not 0, the samples whch the corresponded s SV, b calculated by whchever SV. Consderng some samples that could not be classfed by the hyperplane correctly, convert the optmzaton problem to constran condton by ntroducng slack varable Y ( X b) 1-0 (5) In the constran condton above, there s n 1 mn C C>0. 1 In the result, the most are 0, referrer the sample that s not 0 as support vector. The functon whch the support vector defned s SVM. Usually, we called the tranng sample whch has a small amount samples as support vector that means SVM has the advantages of Fgure. The flow chart of feature extracton. 3

4 4. Classfcaton results and valdaton of 4..1 Image fuson processng precson Table 4. The confuson matrx of maxmum lkelhood classfcaton. Category Water Plough Forestland Construtons Others Amounts water Plough Forestland Construtons Others Amounts Table5. Confuson matrx of Markov dstance method of classfcaton. Category Water Plough Forestland Construtons Others Amounts water Plough Forestland Construtons Others Amounts Table 6. Confuson matrx processed by SVM classfcaton used by spectral characterstcs. Category Plough Forestland Water Construtons Others Amounts Plough Forestland Water Contructons Others Amounts Table 7. Comparng wth classfcaton precson. Methods Overall accuracy (%) Kappa Coeffcent Maxmum lkelhood method Mahalanobs dstance method SVM classfcaton method In order to verfy the applcablty of the SVM whch s used for hgh resoluton mage classfcaton, usng the markov dstance method and the maxmum lkelhood for classfcaton, Calculatng the belongng category and makng the land use classfcaton fgure of test area to the study area, as t s shown n Fg. 3. (a)svm (b)maxmum lkelhood classfcaton (c)mahalanobs dstance classfcaton 4.. Classfcaton accuracy evaluaton Ths experment adopts wdespread confuson matrx method to analyss classfcaton results, selected the test sample randomly correspondng to varous land use types In remote sensng mage, then calculated ts classfcaton confuson matrx and ts related precson ndex respectvely based on the dfferent results of classfcaton above. The results as shown n table 4,5,6. The comparson table shows(table 7): the classfcaton accuracy of applcaton of SVM method s superor to maxmum lkelhood classfcaton and Markov dstance classfcaton method, whch verfed the superorty of support vector machne (SVM) on the 4

5 nonlnear classfcaton problem of small sample. The overall classfcaton accuracy Kappa coeffcent reached 88.79% and Fgure 3. Comparson of classfcaton result. 5 Concluson The method whch realzng the rapd dvson of land use type based on SPOT6 hgh-resoluton satellte data and mage automatc classfcaton technology has mproved the recognton effcency of agrcultural land types. Spot6 data was processed by tranng sample and predctng classfcaton through ther spectral nformaton and the SVM classfcaton method. The classfcaton results shows that not only the algorthm precson of support vector machne s superor to the tradtonal classfcaton algorthms, characterzed by strong adaptablty. The phenomenon of fault classfcaton and mss classfcaton s less, but also t has a hgh degree of stablty. Therefore, n remote sensng mage classfcaton, the selecton of support vector machne method for land use to classfcaton research based on hgh resoluton mages can mprove the classfcaton precson and has a great advantage. References 1. Luo Jancheng, Zhou Chenghu, Yang Yan. Landcover and land-use classfcaton based on remote sensng ntellgent Geo-nterpretng model[j].journal of Natural Resources, 001, 16():179~183.(n Chnese). L Xubn. A revew of the nternatonal researches on land use/land cover change [J].ACTA Geographca Snca, 1996, 51(67):553~557.(n Chnese) 3. Ma Kale, Zhang Wenhu. Object-orented classfcaton approach for remote sensng magery nformaton extracton n loess hlly-gully regon[j].transactons of the Chnese Socety for Agrcultural Machnery011, 4(4): (n Chnese) 4. Zhao Chunhu, Qao Le. Classfcaton of hyperspectral remote sensng mage usng mproved LS-SVM[J]. Appled Scence and Technology, 008, 35(1):44~5.(n Chnese) 5. Fu Wenje, Hong Jny, Ln Mngsen. A method of land use classfcaton from remote sensng mage based on support vector machnes and spectral smlarty scale[j]. Remote Sensng Technology and applcaton, 006, 1(1):5~30.(n Chnese) 6. Du Pejun,Lu Scong, Zheng Hu. Land cover change detecton over mnng areas based on support vector machne [J]. 01, 41():6~67. (n Chnese) 7. Sun Danfeng,Yang Yhong, Lu Shunx. Applcaton of hgh-spatal IKONS remote sensng mages n land use classfcaton and change montorng[j]. Transactons of the CSAE, 00, 18():160~164. (n Chnese) 8. Chen Qhao, Lu Zhmn, Lu Xuguo, et al. Elementorented land-use classfcaton of mnng area by hgh spatal resoluton remote sensng mage[j]. Earth Scence Journal of Chna Unversty of Geoscences, 010, 35(3):453~458. (n Chnese) 9. Zhou Pe, Zhou Shenl. Effect of land use on ecologcal beneft of farm belt n suburbs[j]. Journal of Ecology and Rural Envronment, 007, 3(4): 6~10. (n Chnese) 10. Zhou Y, Wu Juan, L Q, et al. Test and analyss for detectng land use change by usng CBERS-0C satellte mage[j].mneral Exploraton.01,3(5): 688~694. (n Chnese) 11. Ma Lgang, Zhang Lepng, Zheng Jnsong, et al. Land use classfcaton usng ZY1-0Cremote sensng mages [J]. Journal of Zhejang Unversty: Engneerng Scence,013, 47(8):1508~1516. (n Chnese) 1. Wang Hahun, Peng Jaxong, Wu We, et al. A study of evaluaton methods on performance of the multsource remote sensng mage fuson[j].computer Engneerng and Applcatons,003,(5):33~37. (n Chnese) 13. hang Xuegong. Introducton to statstcal learnng theory and support vector machnes[j]. Acta Automatca Snca, 000, 6(1):3~4. (n Chnese) 14. Vapnk V N. The Nature of Statstcal Learnng Theory[M].Berln: Sprnger-Verlag Berln Hedelberg, Hsu C, Ln C.A comparson of methods for multclass support vector machnes[j]. IEEE Transactons on Neural Networks, 00,13 ():415~ Zhang Quanmng, Lu Hujn. Applcaton of LS- SVM n classfcaton of power qualty dsturbances[j]. Proceedngs of the CSEE,008,8(1):106~110. (n Chnese) 17. Yang JajaJang QgangChen Yonglanget al. Lthology dvson for large-scale regon segmentaton based on LS-SVM and hgh resoluton remote sensng mages[j].journal of Chna Unversty of Petroleum(Edton of Natural Scence), 01,36(1):60~67. (n Chnese) 18. Wang Ka,Hou Zhurong,Wang Congl. Intruson detecton based on cross-valdaton SVM[J]. Journal of Test and Measurement Technology 010, 4(5):419~43. (n Chnese) 19. Zhang Jnshu, He Chunyang, Pan Yao, et al. The hgh spatal resoluton RS mage classfcaton based 5

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