Apport des images satellites multi-spectrales (optique et radar) pour la classification des surfaces en herbe.
DOI :
https://doi.org/10.52638/rfpt.2017.311Mots-clés :
Agriculture, Classification, Random Forest, Usage et occupation du sol, Optique, Radar, Formosat-2, TerraSAR-X, Radarsat-2, Alos PALSAR, Surface en herbeRésumé
Les surfaces en herbe jouent un rôle important tant en terme économique qu'environnemental. Elles regroupent une grande diversité de végétation herbacée (pérenne ou éphémère) et occupent une partie importante de l'espace agricole français (zones de pâtures, prairies, milieux agro-naturels présents entre deux cultures principales). Dans ce contexte, cette étude vise à quantifier l'apport de l'imagerie satellitaire multi-spectrale (optique et radar) pour la mise en évidence des surfaces en herbe.
Pour ce faire, des classifications supervisées orientées objet, basées sur un algorithme Random Forest et un zonage majoritaire en post-traitement sont utilisées. Cette étude émane de l'expérience de surveillance des cultures multi-capteurs (MCM'10) menée en 2010 sur une zone de polyculture située dans le sud-ouest de la France, à proximité de Toulouse. L'étude s'appuie sur des images satellites acquises entre le 14 et le 18 avril 2010, avec, en radar, des images en bandes X, C et L (de polarisation HH), en optique, une image Formosat-2, et des données terrain concomitantes acquises le 14 avril 2010. Les résultats montrent que la combinaison des images acquises en bande L (Alos) et en optique (Formosat-2) améliore les performances de la classification (précision globale = 0,85 ; Kappa = 0,81) par rapport à l'utilisation des seuls capteurs radar ou optique. Par ailleurs, la performance F-score obtenue pour les surfaces en herbe varie entre 0,1 pour Formosat-2 ; 0,34 pour la combinaison Formosat-2/ Alos et 0,52 pour Alos. Ainsi, les résultats montrent que la qualité de la classification des surfaces en herbe augmente avec l'augmentation de la longueur d'onde des images utilisées.
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Références
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Atzberger, C., 2013. Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs. Remote Sensing, 5, 4124-4124.
Bastiaanssen W.G.M., Molden D.J., Makin, I.W., 2000. Remote sensing for irrigated agriculture: examples from research and possible applications. Agricultural Water Management, 46, 137-155.
Baup F., Fieuzal R., Marais Sicre C., Dejoux J.-F., le Dantec V., Mordelet P., Claverie M., Demarez V., Hagolle O., Lopes A., Keravec P., Ceschia E., Merlin O., Kidd R., 2012. MCM'10: An experiment for satellite multispectral crop monitoring —From high to low resolution observations, Geoscience and Remote Sensing Symposium, IEEE International, IGARSS 2012. http://www.cesbio.ups-tlse.fr/us/mcm.html.
Baup F., Villa L., Fieuzal R., 2016, Sensitivity of X-band (ï³0, ï§) and optical (NDVI) satellite data to corn height variation, Accepted in Advances in Remote Sensing. March 2016.
Betbeder J., Fieuzal N. and Baup F., 2016, Assimilation of LAI and dry biomass derived from optical and microwave satellite images into and agro-meteorogical model to estimate soybean yield. Accepted in Journal of Selected Topics in Applied Earth Observations and Remote Sensing. March 2016
Betbeder J., Rapinel S.b., Corgne S., Pottier E., Hubert-Moy L., 2015. TerraSAR-X dual-pol time-series for mapping of wetland vegetation. ISPRS Journal of Photogrammetry and Remote Sensing, 107, 90-98.
Blaes X., Vanhalle L., Defourny P., 2005. Efficiency of crop identification based on optical and SAR image time series. Remote Sensing of Environment, 96, 352-365.
Breiman L., 2001. Random Forests. Machine Learning, 45 (1), 5—32.
Chern J.S., Wu A.M., Lin S.F., 2006. Lesson learned from Formosat-2 mission operations. Acta Astronautica 59, 344—350.
Claverie M., Demarez V., Duchemin B., Hagolle O., Ducrot D., Marais-Sicre C., Dejoux J.F., Huc M., Keravec P., Beziat P., Fieuzal R.,
Ceschia E., Dedieu G., 2012. Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data. Remote Sensing of Environment, 124, 844-857.
Comaniciu D., Meer P., 2002. Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 603-619
Congalton R.G., 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35-46.
Corgne S., Dallon D., Mercier G., 2014. Land use and land cover monitoring with multitemporal and multipolarization radarsat data: application to an intensive agricultural area (France), Geoscience and Remote Sensing Symposium (IGARSS), Geoscience and Remote Sensing Symposium, IEEE International, IGARSS2014, 4239 — 4242.
Dabrowska-Zielinska K., Gruszczynska M., Kowalik W., Stankiewicz K., 2002. Application of multisensor data for evaluation of soil moisture. Advances in Space Research, 29, 45—50.
Du P., Samat A., Waske B.r., Liu S., Li Z., 2015. Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features. ISPRS Journal of Photogrammetry and Remote Sensing, 105, 38-53.
Duchemin B., Hadria R., Erraki S., Boulet G., Maisongrande P., Chehbouni A. ,Escadafal R., Ezzahar J., Hoedjes J.C.B., Kharrou M.H., Khabba S., Mougenot B., Olioso A., Rodriguez J.C., Simonneaux V., 2006. Monitoring wheat phenology and irrigation in Central Morocco: on the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices. Agricultural Water Management, 79, 1—27.
Duchemin B.; Fieuzal R.; Augustin Rivera M.., Ezzahar J., Jarlan, L., Cesar Rodriguez J; Hagolle, O., Watts, C., 2015. Impact of Sowing Date on Yield and Water Use Efficiency of Wheat Analyzed through Spatial Modeling and Formosat-2 Images, Remote Sensing, 7, 5951-5979.
Duro D.C., Franklin S.E., Dubé M.G., 2012. 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. Remote Sensing of Environment, 118, 259-272.
Dusseux P., Corpetti T., Hubert-Moy L., Corgne S., 2014. Combined Use of Multi-Temporal Optical and Radar Satellite Images for Grassland Monitoring. Remote Sensing, 6, 6163-6182.
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Fieuzal R., Duchemin B., Jarlan L., Zribi M., Baup F., Merlin O., Hagolle O., Garatuza-Payan J., 2011. Combined use of optical and radar satellite data for the monitoring of irrigation and soil moisture of wheat crops. Hydrology and Earth System Sciences, 15, 1117-1129.
Fieuzal R., Baup F., Marais Sicre C., 2013. Monitoring wheat and rapeseed by using synchronous optical and radar satellite data—from temporal signatures to crop parameters estimation. Advances in Remote Sensing, 2013, 2, 162-180.
Gislason P.O., Benediktsson J.A., Sveinsson J.R., 2006. Random Forests for land cover classification. Pattern Recognition Letters, 27, 294-300.
Hadria R., Duchemin B., Baup F., Le Toan T., Bouvet A., Dedieu G., Le Page M., 2009. Combined use of optical and radar satellite data for the detection of tillage and irrigation operations: Case study in Central Morocco. Agricultural Water Management, 96, 1120-1127.
Hill M.J., Ticehurst C.J., Lee J.S., Grunes M.R., Donald G.E., Henry D., 2005, Integration of optical and radar classifications for mapping pasture type in western Australia. IEEE Transactions on Geoscience and Remote Sensing, 43, 1665-1681.
Hong G., Zhang A., Zho, F., Brisco B., 2014. Integration of optical and synthetic aperture radar (SAR) images to differentiate grassland and alfalfa in Prairie area. International Journal of Applied Earth Observation and Geoinformation, 28, 12-19.
Hussain M., Chen D., Cheng A., Wei H., Stanley D., 2013. Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80, 91-106.
Jacquemoud S., Verhoef W., Baret F., Bacour C., Zarco-Tejada P. J., Asner G. P., 2009. PROSPECT+SAIL models: a review of use for vegetation characterization, Remote Sensing of Environment, 113 (1), S56 — S66.
Jung H.S., Lee W.J., Zhang L., 2014. Theoretical Accuracy of Along-Track Displacement Measurements from Multiple-Aperture Interferometry (MAI). Sensors, 14, 17703-17724.
Lee J.-S., 1981. Speckle analysis and smoothing of synthetic aperture radar images. Computer Graphics and Image Processing, 17, 24-32
Lee J.S., Grunes M.R., Kwok R., 1994. Classification of multi-look polarimetric sar imagery-based on complex wishart distribution. International Journal of Remote Sensing, 15, 2299-2311
Loosvelt L., Peters J., Skriver H., Lievens H., Van Coillie F.M.B., De Baets B., Verhoest, N.E.C., 2012. Random Forests as a tool for estimating uncertainty at pixel-level in SAR image classification. International Journal of Applied Earth Observation and Geoinformation, 19, 173-184
Lu D.; Weng Q., 2007. A survey of image classification methods and techniques for I improving classification performance. International Journal of. Remote Sensing, 28, 823—870.
Marais Sicre C., Baup F., & Fieuzal R., 2014. Determination of the crop row orientations from Formosat-2 multi-temporal and panchromatic images. ISPRS Journal of Photogrammetry and Remote Sensing, 94, 127-142.
Marais Sicre C., Inglada J., Fieuzal R, Baup F., Valero S., Cros J., Huc M., Demarez V., 2016. Early detection of summer crops using high spatial résolution optical images times series, sumbmitted in Remote Sensing.
McNairn H., Champagne C., Shang J., Holmstrom D., Reichert G., 2009. Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories. ISPRS Journal of Photogrammetry and Remote Sensing, 64, 434-449.
McNairn H., Jiali S., Xianfeng J., Champagne C., 2009. The Contribution of ALOS PALSAR Multipolarization and Polarimetric Data to Crop Classification. IEEE Transactions on Geoscience and Remote Sensing , 47, 3981-3992.
Metera E., Sakowski T., SÅ‚oniewski K., Romanowicz B.. 2010. Grazing as a tool to maintain biodiversity of grassland - a review. Animal Science Papers and Reports, vol. 28, (4): 315—334.
Moran M.S., Alonso L., Moreno J.F., Pilar Cendrero Mateo M., Fernando de la Cruz D., Montoro A., 2012. A Radarsat-2 Quad-Polarized Time Series for Monitoring Crop and Soil Conditions in Barrax, (Spain). International Geoscience and Remote Sensing, IEEE, Trans 50, 1057—1070.
Mountrakis, G., Im, J., Ogole, C., 2011. Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 247-259.
Orfeo Toolbox. Disponible en ligne: http://www.orfeo-toolbox.org/ (Accès le 30 Mars 2016).
Pal M., 2005. Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26, 217-222.
Picard G., 2002. Modélisation radar des couverts végétaux. Application à la télédétection de couverts forestiers et agricoles, Thèse de doctorat, Centre d'Études Spatiales de la Biosphère, CESBIO.
Powers D.M.W., 2011. Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation, Journal of Machine Learning Technologies, 2, 37-63.
Rabaute T., Tinel C., Marzocchi Polizzi S., De Boissezon, H., 2012. Kalidéos, des images pour la science: un instrument au service des applications thématiques. Revue Française de Photogrammétrie et de Télédétection 197, 3—9.
Seelan S.K., Laguette S., Casady G.M., Seielstad G.A., 2003. Remote sensing applications for precision agriculture: a learning community approach. Remote Sensing of Environment, 88, 157—169.
Schotten C.G.J., Vanrooy W.W.L., Janssen L.L.F., 1995. Assessment of the capabilities of multitemporal ers-1
Schuster C., Schmidt T., Conrad C., Kleinschmit B., Foerster M., 2015. Grassland habitat mapping by intra-annual time series analysis - Comparison of RapidEye and TerraSAR-X satellite data. International Journal of Applied Earth Observation and Geoinformation, 34, 25-34.
Soria-Ruiz J., McNairn H., 2007. Corn monitoring and crop yield using optical and Radarsat-2 images. Igarss, 2007 IEEE International Geoscience and Remote Sensing Symposium, Vols 1-12: Sensing and Understanding Our Planet, 3655-3658.
Srivastava P.K., Han D., Rico-Ramirez M.A., Bray M., Islam T., 2012. Selection of classification techniques for land use/land cover change investigation. Advances in Space Research, 50, 1250-1265
Tewkesbury A.P., Comber A.J., Tate N.J., Lamb A., Fisher P.F., 2015. A critical synthesis of remotely sensed optical image change detection techniques. Remote Sensing of Environment, 160, 1-14
Ulaby F. T.; Tavakoli A.; Thomas B. A., 1987, Microwave Propagation Constant for a Vegetation Canopy With Vertical Stalks, IEEE Transactions on Geoscience and Remote Sensing, 25 (6), 714-725.
Van Rijsbergen C.J, 1979. Information Retrieval, London: Butterworths.
Vapnik V., 1979. Estimation of Dependences Based on Empirical Data. Nauka, Moscow, 27, 5165—5184, (in Russian) (English translation: Springer Verlag, New York, 1982).
Vertès F., Jeuffroy M.-H., Justes E., Thiébeau P., Corson M., 2010. Connaître et maximiser les bénéfices environnementaux liés à l'azote chez les légumineuses, à l'échelle de la culture, de la rotation et de l'exploitation. Innovations Agronomiques 11 (2010), 25-44
Vignau-Loustau L. ; Huyghe C., 2008. Stratégies fourragères. France Agricole Editions, 2008
Waldner F., Lambert M.J., Li W.J., Weiss M., Demarez, V. Morin D., Marais Sicre, C., Hagolle O., Baret F., Defourny P., 2015. Land Cover and Crop Type Classification along the Season Based on Biophysical Variables Retrieved from Multi-Sensor High-Resolution Time Series. Remote Sensing, 7, 10400-10424.
Atzberger, C., 2013. Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs. Remote Sensing, 5, 4124-4124.
Bastiaanssen W.G.M., Molden D.J., Makin, I.W., 2000. Remote sensing for irrigated agriculture: examples from research and possible applications. Agricultural Water Management, 46, 137-155.
Baup F., Fieuzal R., Marais Sicre C., Dejoux J.-F., le Dantec V., Mordelet P., Claverie M., Demarez V., Hagolle O., Lopes A., Keravec P., Ceschia E., Merlin O., Kidd R., 2012. MCM'10: An experiment for satellite multispectral crop monitoring —From high to low resolution observations, Geoscience and Remote Sensing Symposium, IEEE International, IGARSS 2012. http://www.cesbio.ups-tlse.fr/us/mcm.html.
Baup F., Villa L., Fieuzal R., 2016, Sensitivity of X-band (ï³0, ï§) and optical (NDVI) satellite data to corn height variation, Accepted in Advances in Remote Sensing. March 2016.
Betbeder J., Fieuzal N. and Baup F., 2016, Assimilation of LAI and dry biomass derived from optical and microwave satellite images into and agro-meteorogical model to estimate soybean yield. Accepted in Journal of Selected Topics in Applied Earth Observations and Remote Sensing. March 2016
Betbeder J., Rapinel S.b., Corgne S., Pottier E., Hubert-Moy L., 2015. TerraSAR-X dual-pol time-series for mapping of wetland vegetation. ISPRS Journal of Photogrammetry and Remote Sensing, 107, 90-98.
Blaes X., Vanhalle L., Defourny P., 2005. Efficiency of crop identification based on optical and SAR image time series. Remote Sensing of Environment, 96, 352-365.
Breiman L., 2001. Random Forests. Machine Learning, 45 (1), 5—32.
Chern J.S., Wu A.M., Lin S.F., 2006. Lesson learned from Formosat-2 mission operations. Acta Astronautica 59, 344—350.
Claverie M., Demarez V., Duchemin B., Hagolle O., Ducrot D., Marais-Sicre C., Dejoux J.F., Huc M., Keravec P., Beziat P., Fieuzal R.,
Ceschia E., Dedieu G., 2012. Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data. Remote Sensing of Environment, 124, 844-857.
Comaniciu D., Meer P., 2002. Mean shift: A robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24, 603-619
Congalton R.G., 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35-46.
Corgne S., Dallon D., Mercier G., 2014. Land use and land cover monitoring with multitemporal and multipolarization radarsat data: application to an intensive agricultural area (France), Geoscience and Remote Sensing Symposium (IGARSS), Geoscience and Remote Sensing Symposium, IEEE International, IGARSS2014, 4239 — 4242.
Dabrowska-Zielinska K., Gruszczynska M., Kowalik W., Stankiewicz K., 2002. Application of multisensor data for evaluation of soil moisture. Advances in Space Research, 29, 45—50.
Du P., Samat A., Waske B.r., Liu S., Li Z., 2015. Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features. ISPRS Journal of Photogrammetry and Remote Sensing, 105, 38-53.
Duchemin B., Hadria R., Erraki S., Boulet G., Maisongrande P., Chehbouni A. ,Escadafal R., Ezzahar J., Hoedjes J.C.B., Kharrou M.H., Khabba S., Mougenot B., Olioso A., Rodriguez J.C., Simonneaux V., 2006. Monitoring wheat phenology and irrigation in Central Morocco: on the use of relationships between evapotranspiration, crops coefficients, leaf area index and remotely-sensed vegetation indices. Agricultural Water Management, 79, 1—27.
Duchemin B.; Fieuzal R.; Augustin Rivera M.., Ezzahar J., Jarlan, L., Cesar Rodriguez J; Hagolle, O., Watts, C., 2015. Impact of Sowing Date on Yield and Water Use Efficiency of Wheat Analyzed through Spatial Modeling and Formosat-2 Images, Remote Sensing, 7, 5951-5979.
Duro D.C., Franklin S.E., Dubé M.G., 2012. 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. Remote Sensing of Environment, 118, 259-272.
Dusseux P., Corpetti T., Hubert-Moy L., Corgne S., 2014. Combined Use of Multi-Temporal Optical and Radar Satellite Images for Grassland Monitoring. Remote Sensing, 6, 6163-6182.
Fieuzal R., Baup F., Marais-Sicre C. 2012. Sensitivity of TerraSar-x, Radarsat-2 and Alos satellite radar data to crop variables. IEEE International Geoscience and Remote Sensing Symposium, 3740-3743.
Fieuzal R., Duchemin B., Jarlan L., Zribi M., Baup F., Merlin O., Hagolle O., Garatuza-Payan J., 2011. Combined use of optical and radar satellite data for the monitoring of irrigation and soil moisture of wheat crops. Hydrology and Earth System Sciences, 15, 1117-1129.
Fieuzal R., Baup F., Marais Sicre C., 2013. Monitoring wheat and rapeseed by using synchronous optical and radar satellite data—from temporal signatures to crop parameters estimation. Advances in Remote Sensing, 2013, 2, 162-180.
Gislason P.O., Benediktsson J.A., Sveinsson J.R., 2006. Random Forests for land cover classification. Pattern Recognition Letters, 27, 294-300.
Hadria R., Duchemin B., Baup F., Le Toan T., Bouvet A., Dedieu G., Le Page M., 2009. Combined use of optical and radar satellite data for the detection of tillage and irrigation operations: Case study in Central Morocco. Agricultural Water Management, 96, 1120-1127.
Hill M.J., Ticehurst C.J., Lee J.S., Grunes M.R., Donald G.E., Henry D., 2005, Integration of optical and radar classifications for mapping pasture type in western Australia. IEEE Transactions on Geoscience and Remote Sensing, 43, 1665-1681.
Hong G., Zhang A., Zho, F., Brisco B., 2014. Integration of optical and synthetic aperture radar (SAR) images to differentiate grassland and alfalfa in Prairie area. International Journal of Applied Earth Observation and Geoinformation, 28, 12-19.
Hussain M., Chen D., Cheng A., Wei H., Stanley D., 2013. Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing, 80, 91-106.
Jacquemoud S., Verhoef W., Baret F., Bacour C., Zarco-Tejada P. J., Asner G. P., 2009. PROSPECT+SAIL models: a review of use for vegetation characterization, Remote Sensing of Environment, 113 (1), S56 — S66.
Jung H.S., Lee W.J., Zhang L., 2014. Theoretical Accuracy of Along-Track Displacement Measurements from Multiple-Aperture Interferometry (MAI). Sensors, 14, 17703-17724.
Lee J.-S., 1981. Speckle analysis and smoothing of synthetic aperture radar images. Computer Graphics and Image Processing, 17, 24-32
Lee J.S., Grunes M.R., Kwok R., 1994. Classification of multi-look polarimetric sar imagery-based on complex wishart distribution. International Journal of Remote Sensing, 15, 2299-2311
Loosvelt L., Peters J., Skriver H., Lievens H., Van Coillie F.M.B., De Baets B., Verhoest, N.E.C., 2012. Random Forests as a tool for estimating uncertainty at pixel-level in SAR image classification. International Journal of Applied Earth Observation and Geoinformation, 19, 173-184
Lu D.; Weng Q., 2007. A survey of image classification methods and techniques for I improving classification performance. International Journal of. Remote Sensing, 28, 823—870.
Marais Sicre C., Baup F., & Fieuzal R., 2014. Determination of the crop row orientations from Formosat-2 multi-temporal and panchromatic images. ISPRS Journal of Photogrammetry and Remote Sensing, 94, 127-142.
Marais Sicre C., Inglada J., Fieuzal R, Baup F., Valero S., Cros J., Huc M., Demarez V., 2016. Early detection of summer crops using high spatial résolution optical images times series, sumbmitted in Remote Sensing.
McNairn H., Champagne C., Shang J., Holmstrom D., Reichert G., 2009. Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories. ISPRS Journal of Photogrammetry and Remote Sensing, 64, 434-449.
McNairn H., Jiali S., Xianfeng J., Champagne C., 2009. The Contribution of ALOS PALSAR Multipolarization and Polarimetric Data to Crop Classification. IEEE Transactions on Geoscience and Remote Sensing , 47, 3981-3992.
Metera E., Sakowski T., SÅ‚oniewski K., Romanowicz B.. 2010. Grazing as a tool to maintain biodiversity of grassland - a review. Animal Science Papers and Reports, vol. 28, (4): 315—334.
Moran M.S., Alonso L., Moreno J.F., Pilar Cendrero Mateo M., Fernando de la Cruz D., Montoro A., 2012. A Radarsat-2 Quad-Polarized Time Series for Monitoring Crop and Soil Conditions in Barrax, (Spain). International Geoscience and Remote Sensing, IEEE, Trans 50, 1057—1070.
Mountrakis, G., Im, J., Ogole, C., 2011. Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 247-259.
Orfeo Toolbox. Disponible en ligne: http://www.orfeo-toolbox.org/ (Accès le 30 Mars 2016).
Pal M., 2005. Random forest classifier for remote sensing classification. International Journal of Remote Sensing, 26, 217-222.
Picard G., 2002. Modélisation radar des couverts végétaux. Application à la télédétection de couverts forestiers et agricoles, Thèse de doctorat, Centre d'Études Spatiales de la Biosphère, CESBIO.
Powers D.M.W., 2011. Evaluation: From Precision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation, Journal of Machine Learning Technologies, 2, 37-63.
Rabaute T., Tinel C., Marzocchi Polizzi S., De Boissezon, H., 2012. Kalidéos, des images pour la science: un instrument au service des applications thématiques. Revue Française de Photogrammétrie et de Télédétection 197, 3—9.
Seelan S.K., Laguette S., Casady G.M., Seielstad G.A., 2003. Remote sensing applications for precision agriculture: a learning community approach. Remote Sensing of Environment, 88, 157—169.
Schotten C.G.J., Vanrooy W.W.L., Janssen L.L.F., 1995. Assessment of the capabilities of multitemporal ers-1
Schuster C., Schmidt T., Conrad C., Kleinschmit B., Foerster M., 2015. Grassland habitat mapping by intra-annual time series analysis - Comparison of RapidEye and TerraSAR-X satellite data. International Journal of Applied Earth Observation and Geoinformation, 34, 25-34.
Soria-Ruiz J., McNairn H., 2007. Corn monitoring and crop yield using optical and Radarsat-2 images. Igarss, 2007 IEEE International Geoscience and Remote Sensing Symposium, Vols 1-12: Sensing and Understanding Our Planet, 3655-3658.
Srivastava P.K., Han D., Rico-Ramirez M.A., Bray M., Islam T., 2012. Selection of classification techniques for land use/land cover change investigation. Advances in Space Research, 50, 1250-1265
Tewkesbury A.P., Comber A.J., Tate N.J., Lamb A., Fisher P.F., 2015. A critical synthesis of remotely sensed optical image change detection techniques. Remote Sensing of Environment, 160, 1-14
Ulaby F. T.; Tavakoli A.; Thomas B. A., 1987, Microwave Propagation Constant for a Vegetation Canopy With Vertical Stalks, IEEE Transactions on Geoscience and Remote Sensing, 25 (6), 714-725.
Van Rijsbergen C.J, 1979. Information Retrieval, London: Butterworths.
Vapnik V., 1979. Estimation of Dependences Based on Empirical Data. Nauka, Moscow, 27, 5165—5184, (in Russian) (English translation: Springer Verlag, New York, 1982).
Vertès F., Jeuffroy M.-H., Justes E., Thiébeau P., Corson M., 2010. Connaître et maximiser les bénéfices environnementaux liés à l'azote chez les légumineuses, à l'échelle de la culture, de la rotation et de l'exploitation. Innovations Agronomiques 11 (2010), 25-44
Vignau-Loustau L. ; Huyghe C., 2008. Stratégies fourragères. France Agricole Editions, 2008
Waldner F., Lambert M.J., Li W.J., Weiss M., Demarez, V. Morin D., Marais Sicre, C., Hagolle O., Baret F., Defourny P., 2015. Land Cover and Crop Type Classification along the Season Based on Biophysical Variables Retrieved from Multi-Sensor High-Resolution Time Series. Remote Sensing, 7, 10400-10424.
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2017-08-16
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Marais-Sicre, C., Fieuzal, R., & Baup, F. (2017). Apport des images satellites multi-spectrales (optique et radar) pour la classification des surfaces en herbe. Revue Française de Photogrammétrie et de Télédétection, (215), 25–41. https://doi.org/10.52638/rfpt.2017.311
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