Crop identification by means of seasonal statistics of RapidEye time series

Erik Zillmann, Horst Weichelt BlackBridge

Berlin, Germany erik.zillmann@blackbridge.com

Abstract—Crop classification greatly benefits from the analysis of multi-temporal Earth Observation (EO) data within a growing season utilizing the distinct phenological behavior of each crop. RapidEye’s high repetition rate increases the chances of providing sufficient high resolution image time series offering new ways of classifying crops. This study introduces a supervised decision tree (DT) classification approach using image objects in combination with seasonal statistics of various vegetation indices (VI) for crop identification. The aim of this study is, first, to show the potential of VI seasonal statistics for crop identification, and secondly, to evaluate the relative contribution of each variable to the overall classification accuracy. The results presented in this paper correspond to an area of 625 km² in Saxony-Anhalt, Germany. The cultivated landscape is characterized by large agricultural fields, with winter wheat, canola, corn and winter barley as the main crops. Crop identification accuracies were assessed on the basis of reference fields and the importance of each employed variable is assessed by rule set analysis. The classification accuracy for the test area demonstrates that the proposed approach of multi-temporal image analysis provides spatially detailed and thematically accurate information on the crop type distribution.

Keywords—Multi-seasonal analysis, crop type, object-oriented classification, high resolution data

I. INTRODUCTION Accurate and reliable information about agricultural land

use is important for a variety of societal and environmental reasons. Monitoring agricultural areas is fundamental to design agrarian policies, and to produce accurate yield prediction and water demand estimation. Remote sensing is able to provide spatially detailed information about agricultural land use and its impacts on the environment.

Crop classification, a key factor for agricultural monitoring, benefits significantly from analyzing multi-temporal image data within a growing season utilizing the distinct phenological behavior of different crops and grasslands [1]. Besides the multi-temporal analysis, the spatial aggregation of pixels into image objects and the selection of an appropriate classification method are crucial drivers for the final classification success.

Several studies have already shown the importance of time series analysis of optical imagery for detecting the phenological variations of crops and grasslands over the year [2], [3], [4], [5]. Although crop spectra can be similar at certain growth stages, they also may show differences throughout the

growing season, which can facilitate their discrimination. Even though intra-class variability due to different cultivars decreases the spectral separability between different classes and complicates the use of remote sensing technology, it still has great potential to derive the desired information.

Due to the lack of availability of sensors providing both high spatial and high temporal resolution imagery, most crop mapping studies using multi-temporal optical data had to be based on low to medium spatial resolution images with pixel sizes > 30 m [3], [6], [7], [8], [9], [10]. However, in highly fractured landscapes with heterogeneous cropping patterns of smaller fields, images with higher spatial resolution are needed [11], [12]. BlackBridge’s RapidEye constellation consisting of 5 identical satellites provides high spatial resolution images (5 m) with a theoretical repetition rate of 4-5 days, which makes it highly appropriate for accurate crop type mapping.

Generally, the accuracy of agricultural land cover mapping improves with the number of multi-temporal images [13] and the availability of optimal acquisition dates [4], [14]. In cases where sufficient multi-temporal imagery from high-resolution sensors are not available, a multi-scale analysis of satellite data with different spatial resolutions can be an appropriate alternative to generate classification maps at high spatial resolution [2], [15].

Möller et al. [16] proved that similar acquisition dates do not necessarily correspond to the same phenological phases of crops across regions because of diverse natural conditions and management practices. Even after identifying optimal acquisition periods, it might be impossible to find good quality images (e.g. cloud free) especially for large-area investigations. Zillmann and Weichelt [17] showed that seasonal statistics of various VIs, derived from multi-temporal RapidEye data can be used to aid the analysis process of multi-temporal images of non-optimal acquisition dates to improve the grassland identification.

The optimal spatial units for classification of crops are land cover parcels used for cultivation. These parcels can be identified by object-based image analysis techniques. Lucas et al. [4] and Long et al. [18] achieved good classification results for agricultural landscapes by means of object-based analysis using Landsat time series. Moreover, Lobo et al. [19] was able to improve the accuracy of crop-land and grassland classification by using objects instead of single pixels. Object-based approaches produce a more appealing overall appearance

of land cover classes [20] and, more importamitigate the negative impact of minor mis-reg

Over the last decade the use of noclassifiers has become increasingly commcover classifications because of its independedistribution [22], [23]. Other significant aclassifiers are their ability to adapt to noaccommodate high dimensional data and relevant variables while avoiding the use [24]. DT classifiers are suited to situationcover class is represented by various subtyspectral behavior [25] and are relatively inHughes phenomenon, meaning that the accuwhen the number of features given as inpuexceeds a certain threshold related to the nsamples [26]. Although, DT classifiers are vetraining data and produce unstable results wdifferent training sets [27], different studsuccessfully implemented an object-based combination with a DT classifier for crop-l[20], [28].

This paper presents a multi-temporal, classification approach using seasonal stavegetation indices derived from RapidEye tias input variables for the classifier. A supervclassification was adopted to classify differeC5.0 DT classifier (RuleQuest Research Australia). The results presented in this paptest area of 625 km² in Saxony-Anhalt, classification accuracies and the results of thpresented to show the individual contributiovariables to the classification results.

II. METHODOLOGY

A. Experimental Study Site and Input Data The study site is located in the German

Saxony-Anhalt near the city of Koethen andagricultural land with large fields. It is predoarable crop production that are mainly cocanola and winter barley as well as otheimportance such as potato, hybrid rye, sunfsorghum. The test-site covers an arecorresponding to one RE Level 3A standa(Fig. 1).

The RapidEye satellite system is a cidentical EO satellites with the capability towith five multi-spectral bands, with high rhigh spatial resolution of 5 m. In addition 510 nm), green (520–590 nm), red (630–6(760–850 nm) bands, RapidEye provides (690–730 nm) particularly suitable for vegeta

For the growing season 2011, a time sericloud free images capturing the main phenothe different crops was used (Table I). Fielcrop information of several reference fields be used as training and validation data.

antly, they help to gistrations [21].

on-parametric DT monplace for land ence from variable advantages of DT on-linear relations,

employ only the of redundant data s where one land

ypes with different ndependent to the uracy can decrease ut to the classifier number of training ery sensitive to the when subjected to dies have already image analysis in land mapping [2],

object-based crop atistics of various ime series of 2011 vised object-based

ent crops using the Pty. Ltd., NSW,

er correspond to a Germany. Crop

he DT analysis are on of the different

n Federal State of d is dominated by ominantly used for orn, winter wheat, er crops of lower flower and energy ea of 625 km² ard image product

constellation of 5 o provide imagery repetition rate and to the blue (440–

685 nm) and NIR a red-edge band

ation analysis.

es of seven almost ological phases of d boundaries with were available to

B. Data Processing All images were pre-proces

processing procedure startedMinimum Noise Fraction Filtnoise. Subsequent atmospheric the ATCOR-2/3 software [29] caused by different atmosphecomplete time series was accura

TABLE I. RAPIDEYE SCEN

Acquisition Date Spacecraft March 01, 2011 April 20, 2011 -May 30, 2011 -June 27, 2011 July 16, 2011 -August 24, 2011 September 24, 2011

The next process step is (Table II) for each observationof multi-temporal VI stacks to dinput variables for the land cvegetation dynamics over the gsuch as minimum, maximudeviation and mean absolucalculated. Furthermore, a princof the NDVI time series was peprincipal components (PC) wwell. The PCs isolate the intracomponents, which can be mwith similar vegetation develodiverge in magnitude of greenn

Fig. 1. Study site corresponding to onUsed reference fields are shown in oran

sed consistently. The image pre-d with the application of a ter to remove unwanted image correction was performed using to minimize seasonal variations

eric conditions. Afterwards, the ately co-registered.

NES USED IN THE PRESENT STUDY

View Angle [°] Clod Cover [%] 9.9 0

- 2.9 2 - 2.9 0 13.6 0 - 9.6 3 19.8 0 6.9 0

the calculation of various VIs n and the subsequent generation derive their seasonal statistics as cover classification. To capture growing season statistical values um, range, average, standard ute difference (MAD) were cipal component analysis (PCA) erformed and the first 5 resulting

were used as input variables as a-annual variability into separate

meaningful to differentiate areas opment behavior and areas that ness or in timing of events [30].

ne RapidEye Level 3A tile of 625 km². nge and training data in magenta.

Image objects that serve as classification units were generated based on a bi-temporal image stack (May 30th and August 24th 2011) using the Trimble e-cognition© software. The used multi-resolution segmentation algorithm is parameterized by scale, color and shape [31].

TABLE II. OVERVIEW OF VEGETATION INDICES

Features Formula NDVI (NIR - RED) / (NIR + RED) NDRE (NIR - REDEdge) / (NIR + REDEdge) PSRI (RED – GREEN) / NIR) Cl-RedEdge (NIR / RedEdge) -1 Brightness (RED + GREEN + BLUE) NDVI-PC

The suitability of the parameter settings was checked by visual assessment of the segmentation result and adjusted if necessary. The parameter adjustment is a rather subjective process even though some objective measures for geometric accuracy assessment were published by Möller et al. [32] and Whiteside et al. [33]. The segmentation aims on retrieving meaningful image objects characterizing the in-field heterogeneity to provide various stages of crop development for training purposes, rather than perfectly delineating cultivated parcels. Additionally, the segmentation helps to overcome the limited amount of available reference samples by increasing the number of samples for each crop type.

For each image object the mean and standard deviation of each previously calculated statistical feature as well as the spectral reflectance of the May acquisition were extracted resulting in 70 input variables for the subsequent classification.

C. Classification Classification was performed using the commercial DT

software C5.0 to create a decision tree and to deduce a set of decision rules from training data. C5.0 uses the information gain ratio to estimate the splits at each internal node of the tree and to select the features that form the classifier. To improve the classification performance DT ensembles were realized by means of boosting [25], which combines 10 iterations of DT classifiers into one powerful classifier.

To construct these decision trees 463 training samples representing each classes’ variability were selected from the segments of the reference fields including some additional samples for non-agricultural classes such as forest, water and urban areas. The different classes and the corresponding number of samples are given in Table III.

The number of training samples for each class is almost proportional to the area of reference fields, but some adjustments were made according to the classes’ heterogeneity. A software developed by BlackBridge automatically manages the classification process through C5.0 using a polygon shape file that contains the image objects characterized by spectral variables and the training samples. The decision rules are automatically created based on the training objects and then applied to all remaining image objects in order to classify the land cover types.

TABLE III. CROPS AND ADDITIONAL NON-AGRICULTURAL CLASSES CONSIDERED IN THE CLASSIFICATION SCHEME AND CORRESPONDING

AVAILABLE TRAINING AND REFERENCE SAMPLES.

Crop Class Training Samples [No.]

Validation Samples [No.]

Corn 1 42 375 Winter Wheat 2 56 400 Canola 3 44 545 Winter Barley 4 40 199 Grassland 5 68 237 Potato 6 40 85 Hybrid-Rye 7 35 84 Energy Sorghum 8 26 26 Sunflower 9 15 29 Forest 20 40 574 Water 21 18 113 Impervious 22 39 174

Two different classification trials were performed using different sets of input variables:

C1. Reflectance of five bands of all seven acquisition dates, representing the full information content of the data

C2. Reflectance of five bands of the May acquisition plus seasonal statistics and NDVI-PCA of all seven dates

D. Accuracy Assessment The accuracy assessment was carried out based on all

remaining segments within the reference fields not included into the training data set. Additional validation objects were defined for canola, as they could be easily identified during the crop flowering phase.

The validation data set was adjusted for labeling errors mainly caused by inaccurate field boundaries. Thus, especially segments located along field borders were not considered. Radoux et al. [34] have shown that a validation dataset not influenced by boundary errors is useful to measure the thematic map accuracy. Altogether 2841 validation objects were used. The overall accuracy as well as user’s and producer’s accuracy for each class were obtained by means of a confusion matrix.

Due to the limited availability of independent reference data the chosen sampling approach can lead to somewhat biased accuracy estimation, because the allocation of validation objects does not follow strictly the rules of a basic probability sampling design [35]. A larger part of the validation objects represents the same fields from which the training samples were taken and therefore reflects only the in-field variability. Completely independent validation samples, which allow a more realistic accuracy estimation were only available for grassland and canola as well as for the non-agricultural classes. However, the sampling strategy deemed acceptable to derive accuracy estimates under this restricted availability of reference information.

III. RESULTS The final accuracy assessment of the classification result

for C1 and C2 proves the suitability of VI’s seasonal statistics of multi-temporal RE imagery to improve crop classification. The confusion matrix shows an overall accuracy (OA) of

86.1% for C1 and 90.6% for C2. The detailed analysis of the user’s (UA) and producer’s accuracy (PA) of each individual class reveals a PA of more than 80% for most of the classes and an UA of more than 70% for almost all classes except for hybrid-rye, sorghum and potato (Tables IV and V).

Considering only the agricultural classes it is noteworthy that significant UA differences (Δ > 5%) between C1 and C2 occurred only for four crops. The UA of C2 (based on seasonal statistics) was higher for winter barley (Δ 24.5%) and sunflower (Δ 15.8%) and in contrast, C1 performed better for sorghum (Δ 13%) and hybrid-rye (Δ 9.6%). Regarding the PA, C2 performed significantly better for canola (Δ 22%) and grassland (Δ 14.4%).

The significantly lower omission error of C2 for the grassland classification can be attributed to its different seasonal development compared to crops, which is probably better represented in the seasonal statistics than in the pure spectral reflectance. The large difference in the PA for canola might be mainly attributed to the fact that for canola additional independent validation samples were selected. Objects corresponding to canola fields, which did not contribute to the training sample set, were much more often correctly classified in C2 (Fig. 2). In contrast, the results of both C1 and C2 are more similar, when considering only fields for validation that

contributed to the training data (data not shown). This result probably indicates a more unbiased and realistic accuracy estimation when using validation objects from independent fields, but it also suggests a higher robustness of the rule set established based on the seasonal statistics (C2) than based on pure spectral reflectance values (C1), when the selected training samples are limited in their spatial distribution.

Generally, the confusion between cereals and grassland as well as between cereals and canola is lower when using seasonal statistics as input variables for the classification.

It has to be mentioned that the classification of particular crops is hampered by the existing cropping management system which causes confusion between the different crop types. Especially fields cultivated with winter crops in 2011, show already emerging crops for the next cropping season in the August and September image, which can distort the seasonal statistics as well as the evolution of the pure spectral reflectance. Furthermore, the crop types found in this area complicate the achievement of a better OA, because several crops show similar phenological evolution throughout the season. The three cereals wheat, barley and hybrid-rye have similar seasonal and spectral behavior resulting in corresponding misclassifications. The same holds true for corn and sorghum.

TABLE IV. CONFUSION MATRIX REFERRING TO THE MULTI-TEMPORAL CLASSIFICATION BASED ON REFELECTANCE VALUES (C1)

Reference Class

Cla

ssifi

catio

n

1 2 3 4 5 6 7 8 9 20 21 22 UA 1 331 0 1 0 6 3 1 0 1 7 2 1 93.77 2 1 344 18 3 17 3 2 0 0 0 0 1 88.43 3 0 9 380 5 2 0 3 0 0 0 0 0 95.24 4 5 4 97 178 2 0 0 0 0 0 0 0 62.24 5 8 3 1 6 193 0 1 1 0 0 0 0 90.61 6 7 1 12 0 8 71 0 1 3 0 1 1 67.62 7 1 20 4 2 3 1 62 0 0 0 0 2 65.26 8 15 0 0 0 1 0 2 24 0 0 0 1 55.81 9 1 4 0 0 1 5 0 0 25 0 0 1 67.57

20 2 0 1 0 1 0 2 0 0 567 3 0 98.44 21 0 0 0 0 0 0 0 0 0 0 104 0 100 22 4 15 31 5 3 2 11 0 0 0 3 167 69.29

PA 88.27 86.00 69.72 89.45 81.43 83.53 73.81 92.31 86.21 98.78 92.04 95.98 OA: 86.1

TABLE V. CONFUSION MATRIX REFERRING TO THE MULTI-TEMPORAL CLASSIFICATION BASED ON SEASONAL STATISTICS (C2)

Reference Class

Cla

ssifi

catio

n

1 2 3 4 5 6 7 8 9 20 21 22 UA 1 314 1 0 1 1 1 1 2 0 0 0 0 97.82 2 4 333 6 12 0 2 4 0 0 0 0 0 92.24 3 0 11 500 3 0 3 3 0 0 0 2 1 95.6 4 0 3 22 170 0 0 0 0 0 0 0 1 86.73 5 12 3 6 3 227 6 1 2 2 4 0 0 85.34 6 28 0 0 1 0 70 0 0 1 0 0 0 70 7 3 32 7 9 0 0 64 0 0 0 0 0 55.65 8 11 5 0 0 7 3 0 21 1 0 0 1 42.86 9 3 2 0 0 0 0 0 0 25 0 0 0 83.33

20 0 0 0 0 1 0 0 1 0 570 1 0 99.48 21 0 0 0 0 0 0 0 0 0 0 110 0 100

22 0 10 4 0 1 0 11 0 0 0 0 171 86.8 PA 83.73 83.25 91.74 85.43 95.78 82.35 76.19 80.77 86.21 99.3 97.35 98.28 OA: 90.6

Fig. 2. Classification result of selected canola fields based on multi-temporal reflectance values (C1 - top) and on seasonal statistics (C2 - bottom). Yellow objects are correctly classified as canola objects of fields, which did not contribute to the training sample set. All other objects are misclassified as winter barley (brown), hybrid-rye (blue), winter wheat (orange), sunflower (red), grassland (green) and urban structures (pink).

The apparent confusion of several crop types with the urban class is caused by the vegetation structures included in the urban objects. This leads to mixed spectral characteristics similar to those of crop objects characterized by a spatio-temporal mixture of vegetation and bare soil reflectance. This could be mitigated by the incorporation of texture parameters as additional input variables into the classification process. Nevertheless, in this context the seasonal statistics perform better than the pure spectral reflectance values.

To estimate the contribution of the different variables to the classification result C5.0 generates a percentage of training samples for which the value of that variable is used in predicting its class (Table VI).

TABLE VI. RELEVANCE OF VARIABLES IN CROP CLASSIFICATION BASED ON SESONAL STATISTICS.

Variable a Importance [%] Mean of seasonal mean Brightness 100 Mean of seasonal standard deviation PSRI 100 Mean of seasonal NDVI-PC1 100 Mean of seasonal NDVI-PC3 97 Mean of seasonal MAD PSRI 94 Mean of seasonal range NDVI 92 Mean of seasonal mean PSRI 90 Red Edge reflectance of 30th May 89 Mean of seasonal NDVI-PC4 88 Mean of seasonal minimum PSRI 78 Mean of seasonal maximum NDVI 77 Mean of seasonal standard deviation NDVI 73 Mean of seasonal NDVI-PC5 72 Mean of seasonal mean NDVI 70

a Only the variables that were used by the decision rules when classifying at least 70 % of the training cases were taken into consideration.

Except for the mean of the seasonal mean brightness, the list of most important variables only contained seasonal statistics of the NDVI and PSRI, as well as the seasonal principal components of the NDVI. Additionally, the red-edge reflectance contributed significantly to the crop classification. The other seasonal statistics and reflectance values were of minor relevance although they were needed to improve the classification accuracy.

The high relevance of the PSRI (plant senescence reflectance index) is probably due to its high correlation to the fraction of brown material in the vegetation canopy associated with the crop ripening stage. The relevance of this variable illustrates that the dry matter content is an essential characteristic in discriminating among the different crops and grassland. In contrast, the importance of the seasonal statistics of the NDVI can be attributed to its correlation with green material. The PCs isolate the intra-annual variability of the NDVI into separate components. Therefore, they probably allow for differentiation of areas with similar vegetation development behavior and areas that diverge in magnitude of greenness or in timing of events, which are the main three temporal characteristics in different crop life cycles.

IV. CONCLUSION Seasonal statistics of crop spectral responses combined

with spectral reflectance at critical single dates can significantly improve the chances of crop discrimination. The rule set based on seasonal statistics shows a higher robustness than the one established on pure spectral reflectance values. Further investigation is needed to test whether seasonal statistics as global descriptors are less dependent on specific dates. This would make the entire classification process less susceptible to missing information due to partial cloud coverage.

Only one image every 3-4 weeks throughout the growth cycle seems to be sufficient for accurate crop classification. The flexibility of the proposed workflow allows the integration of multi-scale imagery, so that RapidEye images can be easily combined with lower resolution data such as Landsat 8 or Sentinel-2 images to ensure the necessary multi-temporal

coverage required for agricultural landscape classification, all the while ensuring high spatial resolution.

REFERENCES [1] S. Itzerott and K. Kaden, “Klassifizierung landwirtschaftlicher

Fruchtarten“. PFG, Vol. 2, pp. 109- 120, 2007. [2] T. Esch, A. Metz, M. Marconcine, M. Keil, “Combined use of multi-

seasonal high and medium resolution satelliteimagery for parcel-related mapping of cropland and grassland”, Int. J. of Applied Earth Observation and Geoinformation 28, pp. 230–237, 2014.

[3] V. Simonneaux, B. Duchemin, D. Helson, S. Er-Raki, A. Olioso and A.G. Chehbouni, “The use of high-resolution image time series for crop classification and evapotranspiration estimate over an irrigated area in central Morocco”, Int. J. of Rem. Sens., 29(1), pp. 95–116, 2008.

[4] R. Lucas, A. Rowlands, A. Brown, S. Keyworth and P. Bunting, “Rule-based classification of multi-temporal satellite imagery for habitat and agricultural land cover mapping.” J. of Photogr. & Rem. Sens., 62, pp. 165–185, 2007.

[5] J. T. Bailey and C. ;Boryan, “Remote Sensing Uses in Agriculture at the National Agricultural Statistics Service.” Proc. of ICAS-V, 5th Conf. on Agricultural Statistics, Integrating agriculture into the national statistical system, Kampala, Uganda, 2010.

[6] J. Zhang, Y. Chen, F. Zhang, D. Yue, X. Guo, H. Dong, “Multi-Temporal Remote Sensing Image-Based Extraction on the Crops Planting Information”, Int. J. of Rem. Sens. Applications Vol. 2, 4, 2012

[7] N.J. Singh, M. Kudrat, K. Jain and K. Pandeya;”Cropping pattern of Uttar Pradesh using IRS-P6 (AWiFS) data.” Int. J. of Remote Sensing, Volume 32, Issue 16,pp. 4511-4526, 2011.

[8] M. Punia, P. K. Joshi and M. C. Porwal, “Decision tree classification of land use land cover for Delhi, India using IRS-P6 AWiFS data”; Expert Syst. Appl. 38(5) 5577–5583, 2010, doi:10.1016/j.eswa.2010.10.078.

[9] Y. Shao, R.S. Lunetta, J. Ediriwickrema and J. Iiames, “Monitoring agricultural cropping patterns across the Laurentian Great Lakes Basin using MODIS-NDVI data.” Int. J. of Applied Earth Observation and Geoinformation, Volume 12, Issue 2, pp. 81-88, 2010.

[10] B.D. Wardlow and S.L. Egbert, “Large–area crop mapping using time–series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains.” Rem. Sens. of Environ., 112 (3), 1096–1116, 2008.

[11] C. Conrad, M. Machwitz, G. Schorcht, F. Löw, S. Fritsch and S. Dech, “Potentials of RapidEye time series for improved classification of crop rotations in heterogeneous agricultural landscapes: Experiences from irrigation systems in Central Asia.” In: Proc. of SPIE, 8174., 19.-22. Sep. 2011, Prague, Czech Republic. ISBN 9780819488015.

[12] T. Hoberg and S. Müller, “Multitemporal Crop Type Classification Using Conditional Random Fields and Rapideye Data,” Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXVIII-4/W19, 115-121, doi:10.5194/isprsarchives-XXXVIII-4-W19-115-2011, 2011.

[13] M. Pax-Lenney, C. E. Woodcock, S. A. Macomber, S. Gopal and C. Song, “Forest mapping with a generalized classifier and Landsat TM data.” Rem. Sens. of Environ., 77, 241−250, 2001.

[14] T. G. Van Niel and T. R. McVicar, “Determining temporal windows for crop discrimination with remote sensing: a case study in south-eastern Australia,” Comp. and Electr. in Agri., Vol. 45, Dec. 2004, pp 91–108.

[15] E. Zillmann, A. Gonzalez, E. Montero Herrero, J. van Wolvelaer, T. Esch, M. Keil, H. Weichelt, A. Garzon, "Pan-European Grassland Mapping Using Seasonal Statistics From Multisensor Image Time Series," J. of Selected Topics in App. Earth Obs. and Rem. Sens., IEEE , vol.. no.99, pp. 1,12, doi: 10.1109/JSTARS.2014.2321432.

[16] M. Möller, C. Gläßer and J. Birger, “Automatic interpolation of phenological phases in Germany”. Proc. of MultiTemp 2011, 6th International Workshop on the Analysis of Multi-temporal Remote Sensing Images, pp. 37-40, Trento, Italy, June 2011.

[17] E. Zillmann and H. Weichelt, “Grassland identification using multi-temporal RapidEye image series”, Proc. of MultiTemp 2013, 7th International Workshop on the Analysis of Multi-temporal Remote Sensing Images, Banff, Canada, 25.-27th of June 2013.

[18] J. A. Long, R. L. Lawrence, M. C. Greenwood, L. Marshall and P. R. Miller, “Object-oriented crop classification using multitemporal ETM+ SLC-off imagery and random forest”, GIScience & Remote Sensing, 50:4, pp. 418-436, 2013.

[19] A. Lobo, O. Chic and A. Casterad, “Classification of Mediterranean crops with multi-sensor data: Per-pixel versus per-object statistics and image segmentation.” Int. J. of Rem. Sens. 17, pp. 2385–2400, 1996.

[20] D. C. Duro, S. E. Franklin and M. G. Dube, “A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery”, Rem. Sens. of Env. 118, pp 259–272, 2012.

[21] G. Chen, “Object-based Change Detection: How do image-objects mitigate the negative impacht of misregistration on change accuraccy?”, presented at the 5th Int. Conf. on Object-based Image Analysis – GEOBIA, Thessaloniki, Greece, 21-24 May 2014.

[22] M. Hansen, R. Dubayah and R. DeFries, “Classification trees: an alternative to traditional land cover classifiers.” Int. J. Remote Sens. 17:1075–1081, 1996.

[23] M. A. Friedl and C. E. Brodley, “Decision tree classification of land cover from remotely sensed data.” Rem. Sens. of Environ., 61, pp. 399 – 409, 1997.

[24] J. R. Quinlan, “C4.5: Programs for Machine Learning”. Morgan Kaufmann Publ.: San Mateo, CA, USA, 1993.

[25] R. S. DeFries and J. Cheung-Wai Chan, “Multiple Criteria for Evaluating Machine LearningAlgorithms for Land Cover Classification from Satellite Data.” Rem. Sens. of Env., Vol. 74, pp. 503-515, 2000.

[26] G. Myburgh and A. van Niekerk, “Impact of training size and feature dimensionality on object-based land cover classification: a comparison of three classifiers”, presented at the 5th Int. Conf. on Object-based Image Analysis – GEOBIA, Thessaloniki, Greece, 21-24 May 2014.

[27] L. Breiman, “Bagging predictors.” Machine Learning 24(2), pp. 123–140, 1996.

[28] A. S. Laliberte, E. L. Frederickson, A. Rango, “Combining Decision Trees with Hierarchical Object-oriented Image Analysis for Mapping Arid Rangelands”. Photogr. Eng. and Rem. Sens., 73, 197-207, 2007.

[29] R. Richter, D. Schläpfer, “Atmospheric/Topographic Correction for Satellite Imagery.” Wessling, Germany, 2011.

[30] M. Hall-Beyer, “Comparison of single year and multi year NDVI time series principal componenets over a grassland-forest ecotone”, Proc. of MultiTemp 2001, 1st Int. Workshop on the Analysis of Multi-temporal Remote Sensing Images, pp. 355-362, Trento, Italy, June 2001.

[31] M. Baatz and A. Schape, “Multiresolution segmentation-an optimization approach for high quality multiscale image segmentation.” Ang. Geogr. Informationsverarbeitung XII., AGIT- Symp., Salzburg, Austria, H. Wichmann Verlag, Karlsruhe, pp.12–23, 2000.

[32] M. Möller, J. Birger, A. Gidudu and C. Gläßer, “A framework for the geometric accuracy assessment of classified objects.” Int. J. of Remote Sensing 34 (24), pp. 8685–8698, 2013.

[33] T. G. Whiteside, S. W. Maier, G. S. Boggs, “Area-based and location-based validation of classified image objects.” Int. J. of Applied Earth Observation and Geoinformation 28, 117–130, 2014.

[34] J. Radoux, P. Defourny and P. Bogaert, “Comparison of pixel- and object-based sampling strategies for thematic accuracy assessment,” Proc. of the 2nd Int. Conf. on Object-based Image Analysis – GEOBIA, Calgary, CN, 373p, 2008.

[35] S. V. Stehman and R. L. Czaplewski, “Design and Analysis for Thematic Map Accuracy Assessment: Fundamental Principles”, Rem. Sens. of Environ., 64, pp. 331-344, 1998.