Abstract
Classification of satellite images can be used for land information extraction, i.e., land cover maps, forest maps, industrial maps, residential maps, flooded maps, etc. The classification can be performed using any of the two methods, namely supervised classification method and unsupervised method. However, supervised classification methods require extensive training with existing training datasets. For satellite images, it is difficult to generate training dataset for all the land cover types. Therefore, this paper proposes a novel semi-supervised classification method to classify satellite images. The efficiency of proposed method is tested on satellite images of Delhi and Himalayan regions. Experimental results validate that the proposed method outperforms the existing methods.
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Xu L, Zhang S, He Z, Guo Y. The comparative study of three methods of remote sensing image change detection. In: Geoinformatics, 2009 17th International Conference on. IEEE; 2009. pp. 1–4.
Lillesand T, Kiefer RW, Chipman J. Remote sensing and image interpretation. John Wiley & Sons; 2014.
Blaschke T. Object based image analysis for remote sensing. ISPRS journal of photogrammetry and remote sensing. 2010;65:2–16.
Otukei JR, Blaschke T. Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation. 2010;12:S27–S31.
Srivastava PK, Han D, Rico-Ramirez MA, Bray M, Islam T. Selection of classification techniques for land use/land cover change investigation. Advances in Space Research. 2012;50:1250–1265.
Halder A, Ghosh A, Ghosh S. Supervised and unsupervised land use map generation from remotely sensed images using ant based systems. Applied Soft Computing. 2011;11:5770–5781.
Perumal K, Bhaskaran R. Supervised classification performance of multispectral images. arXiv preprint arXiv:10024046. 2010.
Celik T. Unsupervised change detection in satellite images using principal component analysis and-means clustering. Geoscience and Remote Sensing Letters, IEEE. 2009;6(4):772–776.
Mangal A, Mathur P, Govil R. Trend Analysis in satellite Imagery Using SOFM. Apaji Institute of Mathematics & Computer Technology, Banasthali Vidhyapith, Rajasthan, India. 2005;.
Enderle DIM, WeihJr RC. integrated supervised unsupervised classification method to develop a more accurate land cover classification. In: Journal of the Arkansas Academy of Science; 2005. pp. 65–73.
Giri C, Ochieng E, Tieszen LL, Zhu Z, Singh A, Loveland T, et al. Status and distribution of man-grove forests of the world using earth observation satellite data. Global Ecology and Biogeography. 2011;20(1):154–159.
Li M, Zang S, Zhang B, Li S, Wu C. A review of remote sensing image classification techniques: the role of spatio-contextual information. European Journal of Remote Sensing. 2014;47:389–411.
Moser G, Serpico SB, Benediktsson JA. Land-cover mapping by Markov modelling of spatial-contextual information in very-high-resolution remote sensing images. Proceedings of the IEEE. 2013;101(3):631–651.
Ceccarelli T, Smiraglia D, Bajocco S, Rinaldo S, De Angelis A, Salvati L, et al. Land cover data from Landsat single-date imagery: an approach integrating pixel-based and object-based classifiers. European Journal of Remote Sensing. 2013;46:699–717.
Satellite image; 2005 (accessed December 15, 2015). www.nrsa.gov.in.
Van Laere O, Schockaert S, Dhoedt B. Combining multi-resolution evidence for georeferencing Flickr images. In: Scalable Uncertainty Management. Springer; 2010. pp. 347–360.
Zhu X. Semi-supervised learning literature survey. 2005.
Barreto GA. Time series prediction with the self-organizing map: A review. In: Perspectives of neural-symbolic integration. Springer; 2007. pp. 135–158.
Mather P, Tso B. Classification methods for remotely sensed data. CRC press; 2009.
Malpica A, Matisic JP, Van Niekirk D, Crum CP, Staerkel GA, Yamal JM, et al. Kappa statistics to measure interrater and intrarater agreement for 1790 cervical biopsy specimens among twelve patholo-gists: qualitative histopathologic analysis and methodologic issues. Gynecologic oncology. 2005;99:S38-S52.
Abinader F, de Queiroz AC, Honda DW. Self-organized hierarchical methods for time series forecasting. In: Tools with Artificial Intelligence (ICTAI), 2011 23rd IEEE International Conference on. IEEE; 2011. pp. 1057–1062.
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Pandey, A.C., Kulhari, A. (2018). Semi-supervised Spatiotemporal Classification and Trend Analysis of Satellite Images. In: Bhatia, S., Mishra, K., Tiwari, S., Singh, V. (eds) Advances in Computer and Computational Sciences. Advances in Intelligent Systems and Computing, vol 554. Springer, Singapore. https://doi.org/10.1007/978-981-10-3773-3_35
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DOI: https://doi.org/10.1007/978-981-10-3773-3_35
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