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Jul 9, 2014
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Abstract

Change information of the earth’s surface is becoming more and more important in monitoring the local, regional and global resources and environment. For this reason , Change Detection has an increasing importance in the field of remote sensing. The image acquired by periodical passes of remote sensing satellites over the same areas permit a regular analysis of the changes that occurred on the ground. In this paper a new approach to change detection in very high resolution remote sensing images based on sparse representation of morphological attribute profile is presented. Attribute profiles allows the extraction of geometrical features related to the structure within the scene at different scales. The temporal changes are detected by comparing the geometrical features extracted from the image on each date. Morphological operators have been used in order to decrease the complexity of the image and extract spatial information. The operators are based on mathematical morphology, which is a theory for the analysis of spatial structures based on set theory.

Keywords— Change Detection, sparse representation, morphological attribute profile, remote sensing , very high rersolution imahges.

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Author Biographies

N.C. Anu Sree, Muslim Association College of Engineering, Trivandrum, Kerala, India

Department of Electronics and Communication Engineering,

Shehna Jaleel, Muslim Association College of Engineering, Trivandrum, Kerala, India

Department of Electronics and Communication Engineering,

V. Bhavya, Muslim Association College of Engineering, Trivandrum, Kerala, India

Department of Electronics and Communication Engineering,
How to Cite
Sree, N. A., Jaleel, S., & Bhavya, V. (2014). Change Detection in VHR Images Based on Sparse Representation of Morphological Attribute Profile. International Journal of Emerging Trends in Science and Technology, 1(05). Retrieved from https://igmpublication.org/ijetst.in/index.php/ijetst/article/view/210

References

[1] Nicola Falco, Mauro Dalla Mura, Francesca Bovolo, Jon Atli Benediktsson, and Lorenzo Bruzzone,”Change Detection in VHR Images Basedon Morphological Attribute Profiles” IEEE Transaction. .Geoscience .Remote Sensing., volume. 10, no. 3, May 2013.
[2] Benqin Song, Jun Li, Mauro Dalla Mura, , Peijun Li, Antonio Plaza, José M. Bioucas-Dias, Jon Atli Benediktsson, and Jocelyn Chanussot,” Remotely Sensed Image Classification Using Sparse Representations of Morphological Attribute Profiles”, IEEE transactions on geoscience and remote sensing, vol. 52, no. 8, august 2014.
[3] L. Bruzzone and F. Bovolo, “A novel framework for the design of changedetectionsystems for very-high-resolution remote sensing images,” Proc.IEEE, to be published. [Online]. Available: http://ieeexplore.ieee.org
[4] Y. Chen, N. Nasrabadi, and T. Tran, “Hyperspectral image classification using dictionary-based sparse representation,” IEEE Trans. Geosci. Remote Sens., vol. 49, no. 10, pp. 3973–3985, Oct. 2011.
[5] J. A. Benediktsson, M. Pesaresi, and K. Arnason, “Classification and feature extraction for remote sensing images from urban areas based on morphological transformations,” IEEE Trans. Geosci. Remote Sens., vol. 41, no. 9, pp. 1940–1949, Sep. 2003.
[6] H. Gökhan Akçay and S. Aksoy, “Automatic detection of geospatial objects using multiple hierarchical segmentations,” IEEE Trans. Geosci. Remote Sens., vol. 46, no. 7, pp. 2097–2111, Jul. 2008.
[7] M. Pesaresi and J. A. Benediktsson, “A new approach for the morphologicalsegmentation of high-resolution satellite imagery,” IEEE Trans. Geosci. Remote Sens., vol. 39, no. 2, pp. 309–320, Feb. 2001.
[8] M. Dalla Mura, J. A. Benediktsson, B. Waske, and L. Bruzzone, “Morphological attribute profiles for the analysis of very high resolution images,” IEEE Trans. Geosci. Remote Sens., vol. 48, no. 10, pp. 3747–3762, Oct. 2010.
[9] J. Wright, A. Yang, A. Ganesh, S. Sastry, and Y. Ma, “Robust face recognition via sparse representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 2, pp. 210–227, Feb. 2009.
[10] M. Dalla Mura, A. Villa, J. Benediktsson, J. Chanussot, and L. Bruzzone, “Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis,” IEEE Geosci. Remote Sens. Lett., vol. 8, no. 3, pp. 542–546, May 2011.
[11] L. Bruzzone and D. F. Prieto, “An adaptive parcel-based technique for unsupervised change detection,” Int. J. Remote Sens., vol. 21, no. 4, pp. 817–822, 2000.
[12] M.-D. Iordache, J. Bioucas-Dias, and A. Plaza, “Sparse unmixing of hyperspectral data,” IEEE Trans. Geosci. Remote Sens., vol. 49, no. 6, pp. 2014–2039, Jun. 2011.

[13] F. Bovolo, “A multilevel parcel-based approach to change detection in very high resolution multitemporal images,” IEEE Geosci. Remote Sensi. Lett., vol. 6, no. 1, pp. 33–37, Jan. 2009.

[14] M. Dalla Mura, J. A. Benediktsson, B. Waske, and L. Bruzzone, “Morphological attribute profiles for the analysis of very high resolution images,” IEEE Trans. Geosci. Remote Sens., vol. 48, no. 10, pp. 3747–3762, Oct. 2010.