User menu

The impact of training class proportions on binary cropland classification

Bibliographic reference François, Waldner ; Jacques, Damien Christophe ; Low, Fabian. The impact of training class proportions on binary cropland classification. In: The impact of training class proportions on binary cropland classification, Vol. 8, no.12, p. 1122-1131 (06 Aug 2017)
Permanent URL http://hdl.handle.net/2078.1/188581
  1. ATKINSON P. M., Optimal ground-based sampling for remote sensing investigations: estimating the regional meant, 10.1080/01431169108929672
  2. Bontemps Sophie, Arias Marcela, Cara Cosmin, Dedieu Gérard, Guzzonato Eric, Hagolle Olivier, Inglada Jordi, Matton Nicolas, Morin David, Popescu Ramona, Rabaute Thierry, Savinaud Mickael, Sepulcre Guadalupe, Valero Silvia, Ahmad Ijaz, Bégué Agnès, Wu Bingfang, de Abelleyra Diego, Diarra Alhousseine, Dupuy Stéphane, French Andrew, ul Hassan Akhtar Ibrar, Kussul Nataliia, Lebourgeois Valentine, Le Page Michel, Newby Terrence, Savin Igor, Verón Santiago, Koetz Benjamin, Defourny Pierre, Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2, 10.3390/rs71215815
  3. Breiman Leo, 10.1023/a:1010933404324
  4. Chawla N. V., Journal of Artificial Intelligence Research, 16, 321 (2002)
  5. Chen D., Photogrammetric Engineering and Remote Sensing, 68, 1155 (2002)
  6. Cleveland William S., Devlin Susan J., Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting, 10.1080/01621459.1988.10478639
  7. Foody G.M., Mathur A., A relative evaluation of multiclass image classification by support vector machines, 10.1109/tgrs.2004.827257
  8. Foody Giles M., Mathur Ajay, Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification, 10.1016/j.rse.2004.06.017
  9. Foody G. M., Arora M. K., An evaluation of some factors affecting the accuracy of classification by an artificial neural network, 10.1080/014311697218764
  10. Gislason Pall Oskar, Benediktsson Jon Atli, Sveinsson Johannes R., Random Forests for land cover classification, 10.1016/j.patrec.2005.08.011
  11. Hagolle Olivier, Sylvander Sylvia, Huc Mireille, Claverie Martin, Clesse Dominique, Dechoz Cécile, Lonjou Vincent, Poulain Vincent, SPOT-4 (Take 5): Simulation of Sentinel-2 Time Series on 45 Large Sites, 10.3390/rs70912242
  12. Huang C., Davis L. S., Townshend J. R. G., An assessment of support vector machines for land cover classification, 10.1080/01431160110040323
  13. Japkowicz N., Intelligent Data Analysis, 6, 429 (2002)
  14. Li Congcong, Wang Jie, Wang Lei, Hu Luanyun, Gong Peng, Comparison of Classification Algorithms and Training Sample Sizes in Urban Land Classification with Landsat Thematic Mapper Imagery, 10.3390/rs6020964
  15. Blagus Rok, Lusa Lara, Class prediction for high-dimensional class-imbalanced data, 10.1186/1471-2105-11-523
  16. Mather Paul M., Koch Magaly, Computer Processing of Remotely-Sensed Images : An Introduction, ISBN:9780470666517, 10.1002/9780470666517
  17. Matton Nicolas, Canto Guadalupe, Waldner François, Valero Silvia, Morin David, Inglada Jordi, Arias Marcela, Bontemps Sophie, Koetz Benjamin, Defourny Pierre, An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series, 10.3390/rs71013208
  18. Millard Koreen, Richardson Murray, On the Importance of Training Data Sample Selection in Random Forest Image Classification: A Case Study in Peatland Ecosystem Mapping, 10.3390/rs70708489
  19. Pal Mahesh, Mather P.M., Assessment of the effectiveness of support vector machines for hyperspectral data, 10.1016/j.future.2003.11.011
  20. Prati Ronaldo C., Batista Gustavo E. A. P. A., Silva Diego F., Class imbalance revisited: a new experimental setup to assess the performance of treatment methods, 10.1007/s10115-014-0794-3
  21. VANNIEL T, MCVICAR T, DATT B, On the relationship between training sample size and data dimensionality: Monte Carlo analysis of broadband multi-temporal classification, 10.1016/j.rse.2005.08.011
  22. Vapnik V. N., The Nature of Statistical Learning Theory. Statistics for Engineering and Information Science New York: Springer-Verlag (2000)
  23. Waldner François, Canto Guadalupe Sepulcre, Defourny Pierre, Automated annual cropland mapping using knowledge-based temporal features, 10.1016/j.isprsjprs.2015.09.013
  24. Waldner François, De Abelleyra Diego, Verón Santiago R., Zhang Miao, Wu Bingfang, Plotnikov Dmitry, Bartalev Sergey, Lavreniuk Mykola, Skakun Sergii, Kussul Nataliia, Le Maire Guerric, Dupuy Stéphane, Jarvis Ian, Defourny Pierre, Towards a set of agrosystem-specific cropland mapping methods to address the global cropland diversity, 10.1080/01431161.2016.1194545
  25. Waldner François, Defourny Pierre, Where can pixel counting area estimates meet user-defined accuracy requirements?, 10.1016/j.jag.2017.03.014
  26. Zhu Zhe, Gallant Alisa L., Woodcock Curtis E., Pengra Bruce, Olofsson Pontus, Loveland Thomas R., Jin Suming, Dahal Devendra, Yang Limin, Auch Roger F., Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative, 10.1016/j.isprsjprs.2016.11.004