Comparison of support vector machine, artificial neural network, and spectral angle mapper algorithms for crop classification using LISS IV data

International Journal of Remote Sensing

Volumn 36, Issue 6, 2015, Pages 1604-1617

  Kumar, Pradeep ^a   Gupta, Dileep Kumar ^a   Mishra, Varun Narayan ^a   Prasad, Rajendra ^a  

^a BANARAS HINDU UNIVERSITY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

CROPS; DATA ACQUISITION; NEURAL NETWORKS; QUALITY CONTROL; SUPPORT VECTOR MACHINES;

CLASSIFICATION ALGORITHM; CROP CLASSIFICATION; OVERALL ACCURACIES; RELATIVE ACCURACY; SEPARABILITY ANALYSIS; SPATIAL RESOLUTION; SPECTRAL ANGLE MAPPERS; TRAINING SAMPLE;

CLASSIFICATION (OF INFORMATION);

ACCURACY ASSESSMENT; ALGORITHM; ARTIFICIAL NEURAL NETWORK; CROP PRODUCTION; DATA ACQUISITION; IMAGE CLASSIFICATION; LISS; SATELLITE DATA; SATELLITE IMAGERY; SPECTRAL ANALYSIS;

INDIA; UTTAR PRADESH; VARANASI;

EID: 84926213411     PISSN: 01431161     EISSN: 13665901     Source Type: Journal    
DOI: 10.1080/2150704X.2015.1019015     Document Type: Article

References (29)

1. Alganci, U., E. Sertel, M. Ozdogan, and C. Ormeci. 2013. “Parcel Level Identification of Crop Types Using Different Classification Algorithms and Multi-Resolution Imagery in South Eastern Turkey.” Photogrammetric Engineering and Remote Sensing 79: 1053–1065. doi:10.14358/PERS.79.11.1053.
2. Atkinson, P. M., and A. R. L. Tatnall. 1997. “Introduction Neural Networks in Remote Sensing.” International Journal of Remote Sensing 18: 699–709. doi:10.1080/014311697218700.
3. Atzberger, C., and F. Rembold. 2013. “Mapping the Spatial Distribution of Winter Crops at Sub-Pixel Level Using AVHRR NDVI Time Series and Neural Nets.” Remote Sensing 5: 1335–1354. doi:10.3390/rs5031335.
4. Chakraborty, M., and S. Panigrahy. 1997. “Comparative Performance of Per-Pixel Classifiers Using ERS-1 SAR Data for Classification of Rice Crop.” Journal of the Indian Society of Remote Sensing 25: 155–161. doi:10.1007/BF03024216.
5. Clark, M. L., D. A. Roberts, and D. B. Clark. 2005. “Hyperspectral Discrimination of Tropical Rain Forest Tree Species at Leaf to Crown Scales.” Remote Sensing of Environment 96: 375–398. doi:10.1016/j.rse.2005.03.009.
6. Cohen, J. 1960. “A Coefficient of Agreement for Nominal Scales.” Educational and Psychological Measurement 20: 37–46. doi:10.1177/001316446002000104.
7. Cruz-Ramírez, M., C. Hervás-Martínez, M. Jurado-Expósito, and F. López-Granados. 2012. “A Multi-Objective Neural Network Based Method for Cover Crop Identification from Remote Sensed Data.” Expert Systems with Applications 39: 10038–10048. doi:10.1016/j.eswa.2012.02.046.
8. Foody, G. M. 2004. “Thematic Map Comparison: Evaluating the Statistical Significance of Differences in Classification Accuracy.” Photogrammetric Engineering and Remote Sensing 70: 627–633. doi:10.14358/PERS.70.5.627.
9. Foody, G. M., and M. K. Arora. 1997. “An Evaluation of Some Factors Affecting the Accuracy of Classification by an Artificial Neural Network.” International Journal of Remote Sensing 18: 799–810. doi:10.1080/014311697218764.
10. Foody, G. M., and A. Mathur. 2004. “A Relative Evaluation of Multiclass Image Classification by Support Vector Machines.” IEEE Transactions on Geoscience and Remote Sensing 42: 1335–1343. doi:10.1109/TGRS.2004.827257.
11. Girouard, G., A. Bannari, A. El Harti, and A. Desrochers. 2004. “Validated Spectral Angle Mapper Algorithm for Geological Mapping: Comparative Study between Quickbird and Landsat-TM.” www.isprs.org/istambul 2004/comm4/papers/432 pdf
12. Huang, C., L. S. Davis, and J. R. G. Townshend. 2002. “An Assessment of Support Vector Machines for Land Cover Classification.” International Journal of Remote Sensing 23: 725–749. doi:10.1080/01431160110040323.
13. Kavzoglu, T., and P. M. Mather. 1999. “Pruning Artificial Neural Networks: An Example Using Land Cover Classification of Multi-Sensor Images.” International Journal of Remote Sensing 20: 2787–2803. doi:10.1080/014311699211796.
14. Kavzoglu, T., and P. M. Mather. 2003. “The Use of Back Propagating Artificial Neural Networks in Land Cover Classification.” International Journal of Remote Sensing 24: 4907–4938. doi:10.1080/0143116031000114851.
15. Lillesand, T. M., and R. W. Kiefer. 1999. Remote Sensing and Image Interpretation. New York: Wiley.
16. Lu, D., and Q. Weng. 2007. “A Survey of Image Classification Methods and Techniques for Improving Classification Performance.” International Journal of Remote Sensing 28: 823–870. doi:10.1080/01431160600746456.
17. Mas, J. F., and J. J. Flores. 2008. “The Application of Artificial Neural Networks to the Analysis of Remotely Sensed Data.” International Journal of Remote Sensing 29: 617–663. doi:10.1080/01431160701352154.
18. Omkar, S. N., J. Senthilnath, D. Mudigere, and M. M. Kumar. 2008. “Crop Classification Using Biologically Inspired Techniques with High Resolution Satellite Image.” Journal of the Indian Society of Remote Sensing 36: 175–182. doi:10.1007/s12524-008-0018-y.
19. Pal, M., and G. M. Foody. 2010. “Feature Selection for Classification of Hyperspectral Data by SVM.” IEEE Transactions on Geoscience and Remote Sensing 48: 2297–2307. doi:10.1109/TGRS.2009.2039484.
20. Pal, M., A. E. Maxwell, and T. A. Warner. 2013. “Kernel-Based Extreme Learning Machine for Remote-Sensing Image Classification.” Remote Sensing Letters 4: 853–862. doi:10.1080/2150704X.2013.805279.
21. Sesha Sai, M. V. R., and P. V. Narasimha Rao. 2008. “Utilization of Resourcesat-1 Data for Improved Crop Discrimination.” International Journal of Applied Earth Observation and Geoinformation 10: 206–210. doi:10.1016/j.jag.2008.02.009.
22. Sohn, Y., E. Morgan, and F. Gurri. 1999. “Deforestation in North-Central Yucatan (1985–1995): Mapping Secondary Succession of Forest and Agricultural Land Use in Sotuta Using the Cosine of the Angle Concept.” Photogrammetric Engineering and Remote Sensing 65: 947–958.
23. Sohn, Y., and N. S. Rebello. 2002. “Supervised and Unsupervised Spectral Angle Classifiers.” Photogrammetric Engineering and Remote Sensing 68: 1271–1280.
24. Srivastava, P. K., D. Han, M. A. Rico-Ramirez, M. Bray, and T. Islam. 2012. “Selection of Classification Techniques for Land Use/Land Cover Change Investigation.” Advances in Space Research 50: 1250–1265. doi:10.1016/j.asr.2012.06.032.
25. Su, L., M. J. Chopping, A. Rango, J. V. Martonchik, and D. P. C. Peters. 2007. “Support Vector Machines for Recognition of Semi-Arid Vegetation Types Using MISR Multi-Angle Imagery.” Remote Sensing of Environment 107: 299–311. doi:10.1016/j.rse.2006.05.023.
26. Swain, P. H., and S. M. Davis. 1978. Remote Sensing: The Quantitative Approach. New York: McGraw Hill Book Company.
27. Vapnik, V. N. 1998. Statistical Learning Theory. New York: Wiley.
28. Waske, B., and J. A. Benediktsson. 2007. “Fusion of Support Vector Machines for Classification of Multisensor Data.” IEEE Transactions on Geoscience and Remote Sensing 45: 3858–3866. doi:10.1109/TGRS.2007.898446.
29. Yonezawa, C. 2007. “Maximum Likelihood Classification Combined with Spectral Angle Mapper Algorithm for High Resolution Satellite Imagery.” International Journal of Remote Sensing 28: 3729–3737. doi:10.1080/01431160701373713.