ISPRS Archive Vol. XXXVIII, Part W9, "Core Spatial Databases - Updating, Maintenance and Services from Theory to Practice", Haifa, Israel, 2010

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1 CLASSIICATION O ARMLAND USING MULTITEMPORAL AERIAL IMAGES S. Müller a, C. Hepke a, K. Pakzad b a IPI Insttute o Photogrammetry and GeoInormaton, Lebnz Unverstät Hannover, Nenburger Str. 1, Hannover, Germany, {mueller, hepke}@p.un-hannover.de b ETAS ernerkundung Technologetranser GmbH, Oststraße 2-18, Münster, Germany, kan.pakzad@etas.com Commsson IV, WG IV/2 KEY WORDS: Agrculture, crop, vegetaton, classcaton, aeral, multtemporal ABSTRACT: It s well known that there s a growng need or consstent and up-to-date GIS-data at varous scales or admnstratve and regulatory applcatons. Especally armland classes are o hgh nterest n ths context. A new automatc method or the classcaton o crops s descrbed. The method s based on sequences o dgtal aeral orthophotos wth a ground samplng dstance o 0.17m. The appled mage sequence conssts o twelve mages o the same regon wthn one vegetaton perod. Expert knowledge about the crops together wth extracted eatures leads to temporal models or each crop. The temporal change o the eatures along wth a changng relevance o a eature s consdered. The temporal models are appled durng classcaton that s based on a weghtng uncton. The approach s tested on a test ste o about 700ha and acheves correct classcaton rates better than 90%. 1. INTRODUCTION The background o the paper s the observaton and classcaton o armland objects contaned n present-day GIS data. Increasng demands or current and detaled normaton n GIS requre the employment o automatc mage processng technques to reduce manual eorts when nterpretng aeral mages. Possble applcatons or such automatc processng are change detecton, montorng o allow land and o eroson preventng actons. or the envsaged task, today mage sequences o one vegetaton perod need to be manually nterpreted by human operators. Ths paper descrbes an automatc classcaton approach usng multtemporal aeral mages wth a GSD o 0.17m and the spectral bands RGB. The approach s desgned or the observaton and classcaton o agrculture crops lke summer and wnter cereals, rape, meadow and orest. The classcaton s based on objects, where the borders are taken rom an exstng GIS. The worklow contans a eature extracton that s perormed on sngle epochs ollowed by a classcaton step that regards the calculated eatures o all epochs together. Consderable eorts have been carred out montorng crop phenology rom remote-sensng magery n the last years [Törmä et al., 2007] [Shua et al., 2008] [Vatsava, 2009]. However, only ew works that use a temporal model to gude the classcaton can be ound. Current approaches [etosa et al., 2009] apply statstc models lke Hdden Markov Models (HMM) that contan and model the temporal behavour o the nvestgated crop classes. The statstc approaches model the temporal knowledge about the classes o nterest mplct and have to be traned wth a sucent sample o test data. Hence the classcaton n the proposed approach s based on explctly ormulated multtemporal models that are set up or each class separately. These models contan weghts or each eature dependng on the observaton tme and represent the relevance o a eature over tme. The classcaton tsel s done by calculatng an evaluaton measure whch regards all avalable mages o a scene durng one vegetaton perod. The hghest evaluaton measure ndcates the classcaton result or a armland object. The method s tested on a test ste o about 700ha ncludng 23 reerence armland objects o derent classes. The correspondng multtemporal mage materal conssts o 12 aeral mage mosacs o the vegetaton perod between 2005 and or the test ste correct classcaton rates o about 90% are acheved. 2. DATA USED AND TEST SITE or the mplemented approach, a rural test ste that s ntensvely cultvated wth derent crops s chosen near the German cty Rostock n the state Mecklenburg-Western Pomerana. An overvew o the test ste s shown n gure 1. The aeral mages were captured rom a small arcrat wth the low-cost system PI [Grenzdörer, 2005], developed at the Unversty Rostock and the Unversty Greswald. The system has the advantage to be relatvely weather ndependent. It can be used even n cloudy weather condtons. Thereore the acquston dates can be predened by the user qute accurately. 70

2 gure 1 Aeral mage o the test ste near Rostock (the red boxes denote the 23 reerence and the 18 other elds, see secton 3.2) gure 2 Acquston dates o the mage sequence Two lghts were made at each nvestgated date, the rst wth a small lyng heght resultng n an mage resoluton o 0.17m, the second wth a larger heght resultng n an mage resoluton o 0.47m. The used sensor has an nrared channel, but due to calbraton problems, only the bands RGB could be used here. The mage sequence conssts o mages taken n the years 2005 and 2006 at the ollowng dates: , , , , , , , , , , and The tme shts between two mages vary as depcted n gure 2. Durng the growth perods o the cultvated plants more mages were taken than durng the remanng tme. The nvestgated crops and land use types n the test ste consst o eght classes; the classes corn, rape, wnter wheat and meadow are domnant (re. Table 1). crop type/ land cover type number o elds sprng barley 1 corn 8 wnter wheat 3 oat 1 wnter rye 1 rape 5 orest 1 meadow 3 Table 1 Crops and land use classes o nterest and number o reerence elds (see secton 3.2) 3. APPROACH The goal o the proposed approach s the assgnment o a crop or land use class to regons wth pre-dened borders. The segmentaton o the regons s taken rom an exstng GIS and not subject o the paper. The approach works as ollows: rst numercal eatures are calculated or each regon. To classy the regons nto the classes o nterest, temporal models are ntroduced, that represent the temporal behavour o each nvestgated crop. A numercal eature vector s calculated or each regon and or each mage o the mage sequence. All eature vectors together wth the temporal models or each crop are consdered smultaneously durng classcaton. Here a comparson and evaluaton based on the actual eature vectors and the temporal models s done, resultng n the most probable nterpretaton result. The next sectons descrbe n detal the eature extracton, the generaton o the temporal models and the classcaton method. 3.1 eature extracton The eatures that were chosen orm the bass or the nterpretaton o the mage sequence. They should represent the relevant characterstcs that are also observable by a human operator or the classes o nterest. Important tems are the coverage degree, the growth stages and the human percepton o the crops. Ths led us to choose the ollowng eatures o spectral as well as structural eatures: Spectral eatures o green coloured pxel rato o sand coloured pxel rato o orange coloured pxel rato o purple coloured pxel rato o shade coloured pxel rato Structural eatures o parallel lnes The spectral eatures represent the typcal colour behavour o a class. or sprng barley an example o the colour behavour s gven n gure 3. The colour changes rom sand colour n sprngtme to green n the summer and back to sand colour durng autumn. 71

3 The behavour o the colour corresponds to the coverage degree (cp. gure 4) and the growth stages (cp. gure 5) or the specc crop. The colour ratos are calculated accordng to Equaton 1 as the rato o pxels belongng to a user dened colour class to the sum o all pxels o a regon. gure 3 Image sequence o sprng barley (2006) gure 4 Coverage degree or nvestgated crops (2006) gure 6 Drecton dependency o local contrast 3.2 Temporal model gure 5 Growth stages or nvestgated crops (2006) color = regon pxel color class (1) regon pxel The detecton o parallel lnes caused by agrcultural machnes s based on the determnaton o texture parameters. or t the grey level co-occurrence matrx s calculated that delvers the local contrast n the mage n the our drectons 0, 45, 90 and 135. I no parallel lnes exst n the mage (cp. gure 6 lower row), the sgn o the local contrast derence changes arbtrarly rom one drecton to the next consderng all drectons. I parallel lnes exst (cp. gure 6 upper row) the sgn o the local contrast derence changes only once consderng all drectons. or each class o nterest a temporal model s set up that represents rst the behavour o all ntroduced eatures over tme and second the relevance o each eature over tme. That means that one specc eature can be relevant or example n summer only, whereas another one only durng sprngtme. In gure 7 the systematcs o the used temporal model s llustrated. The temporal models or the derent classes o nterest s determned based on the evaluaton o 23 reerence elds that are marked n gure 1 wth numbers and red boxes. Some o these elds were perodcally nspected durng the years 2005 and In addton expert knowledge about the crop growth was used to set up the temporal models. In gure 8 the resultng temporal model or the crop sprng barley s llustrated. The grey box represents the relevance o 100% or all eatures between the 1 st Aprl and 30 th September. Ths smplcaton o the relevance dstrbuton was chosen to smply the set up o the temporal models. In comparson to gure 3 the elds appear n sand colour sprng, n green durng the summer, n purple durng a short tmeslot n July, n orange n August and agan n sand colour n September. Parallel lnes are vsble rom May to October. The summer cereals sprng barley, corn (cp. gure 9) and oat (cp. gure 10) have a lve cycle rom sprng to autumn n one and the same year. The crops wnter wheat (cp. gure 11) and wnter rye (cp. gure 12) that are part o wnter cereals as well as rape (cp. gure 13) have a le cycle that begns n autumn and ends n the autumn o the subsequent year. 72

4 October July tme January Aprl eature relevance gure 7 Temporal model gure 11 Temporal model or wnter wheat gure 8 Temporal model or sprng barely gure 12 Temporal model or wnter rye gure 9 Temporal model or corn gure 13 Temporal model or rape 3.3 Classcaton method gure 10 Temporal model or oat The classcaton s done wth a weghtng uncton (re. Equaton 2). A value Q C s calculated or each class o nterest a regon could belong to. The class wth the hghest qualty value ndcates the classcaton result. The eatures are enumerated rom 1 to. or one value Q C all eatures and all dates are consdered as can be seen n Equaton 2. The number o used dates s not predened and can be adapted accordng to actual avalable data. Q C where Q C C = = 1 1 W = 1 W t n = t1 R ( 1 Δ ) t n = t1 qualty value belongng to class C assumed class or regon R, (2) 73

5 W t 1, t R, number o eatures current eature weght or current eature and class C rst and last used date current date relevance o current eature at date or class C derence between observaton and model or eature, class C and tme [0..1] 4. RESULTS The results are based on aeral mages wth a spatal resoluton o 0.17m and the bands RGB. The test ste s llustrated n gure 1 and has an extent o 5km 1.4km. Insde the test area 41 test elds are chosen (marked wth red boxes n gure 1). 23 o these elds are enumerated and represent the reerence sample or the approach. The remanng 18 test elds belong to classes out o consderaton. 9 elds o the reerence sample were perodcally nspected n 2005 and The remanng 14 elds are manually/ vsually classed. In the ollowng the descrbed classcaton method s appled to the data. or the tranng o the temporal models dsjont regons to the test sample were used. The classcaton result s llustrated n Table 2. The overall correct classcaton rate amounts to 91.3%, only two elds were msclassed n ths test: one corn eld was classed as meadow and one wnter wheat eld was classed as rape. crop type/ land cover number o elds wrong c lassed correct cla sscaton type elds rate sprng barley corn % wnter wheat % oat wnter rye rape 5-100% orest m eadow 3-100% Table 2 Classcaton result To test the sgncance o sngle eatures, the same test sample was classed agan wth the descrbed classcaton method. The eature parallel lnes s not consdered or ths test. The results are llustrated n Table 3. A strong decrease o the overall correct classcaton rate rom 91.3% to 69.6% s observable. Now the class meadow could not at all be classed n ths test. The meadow elds are msclassed as rape n ths test. crop type/ land cover number o elds wrong classed elds correct cla sscaton type rate sprng barley corn % wnter wheat % oat wnter rye 1 1 0% rape 5-100% orest m eadow 3 3 0% Table 3 Classcaton result wthout eature parallel lnes 5. CONCLUSIONS AND OUTLOOK or the classcaton o armland usually multspectral approaches are appled that work wth spectral sgnatures and are traned wth learnng samples. I an mage sequence s avalable, most oten the data s stacked to a multlayer artcal mage that s due to the number o bands dcult to classy. The presented approach works wth temporal models that nclude expert knowledge about the crops consderng growth stages and observable eatures that are explctly ormulated. Due to the low mpact o the nput data on the models, they are easy to set up, to adapt and to expand. The classcaton result usng all ntroduced eatures acheves correct classcaton rates better than 90%. Obvously, the used test sample s qute small and the approach has to be tested and evaluated urther. The transerablty o the temporal models rom one year to the next wth changng clmate condtons s at the moment not consdered. The results show that the perormance o the approach strongly depends on the eature collecton: omttng o a sngle eature can aect the correct classcaton rate to a large extent. In uture the approach should be tested on addtonal crops to ncrease the number o classes that can be classed. Moreover the temporal models can be rened and expanded based on addtonal bands and eatures, especally the NDVI. Also the approach should be appled to hgh resoluton satellte data ncludng the search o sutable eatures under consderaton o the reduced spatal resoluton. 6. LITERATURE etosa R.Q., Costa G.A.O.P., Mota G.L.A., Pakzad K., Costa M.C.O., 2009: Cascade multtemporal classcaton based on uzzy Markov chans. In: ISPRS Journal o Photogrammetry and Remote Sensng, Vol. 64(2), pp Grenzdörer G., 2005: lexble Hgh Resoluton Urban Remote Sensng wth PI A Dgtal Low Cost System. In: Proceedngs o the ISRPS WG VII/1 Human Settlements and Impact Analyss Urban Remote Sensng Conerence, Tempe USA. Shua Y., Schaa C.B., Strahler A.H., L X., Gao., Lu J., Wole R.E., Wang J., Zhang X., Zhu, Q, 2008: Montorng vegetaton phenology usng mproved MODIS products. In: Proceedngs - SPIE the Internatonal Socety or Optcal Engneerng, VOL 6833; PART 2, pp X X.9 Törmä M., Ranknen K., Härmä P., 2007: Usng phenologcal normaton derved rom MODIS-data to ad nutrent modelng. In: Geoscence and Remote Sensng Symposum, pp Vatsava R. R., 2009: Phenologcal event detecton rom multtemporal mage data. In: SensorKDD '09: Proceedngs o the Thrd Internatonal Workshop on Knowledge Dscovery rom Sensor Data, Pars, rance, pp