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A Heterogeneous Approach to Extract Information from High Resolution Satellite Images

V. Vijaya Samundeeswri*

Professor, Department of Computer Science, Women's Christian College, India.

Periodicity:July - September'2017
DOI : https://doi.org/10.26634/jip.4.3.13916

Abstract

Information extraction from high resolution satellite images is very important for various purposes. The extracted information depicts the factual data about the identified objects, their positions, sizes, and the inter relationship between the objects. Here, the information extraction highlights extracting the general segmentation of Open Area, Water, Soil, Cloud and Snow, Buildings, Vegetation, Water Bodies, Road Center-lines, and so on, without any human intervention or interpretation. This paper presents a heterogeneous approach to extract information in a fully automatic manner using an algorithm, which employs satellite image processing of higher resolution satellite images taken from IRS (Indian Remote Sensing). Thus working on this approach has brought a futuristic research, which reveals how extracted information can be maneuvered.

Keywords

Object Identification, Remote Sensing, Satellite Image Processing

How to Cite this Article?

Samundeeswari, V. (2017). A Heterogeneous Approach to Extract Information from High Resolution Satellite Images. i-manager’s Journal on Image Processing, 4(3), 1-7. https://doi.org/10.26634/jip.4.3.13916

References

[1]. Gruen, A., & Li, H. (1997). Semi-automatic linear feature extraction by dynamic programming and LSBsnakes. Photogrammetric Engineering and Remote Sensing, 63(8), 985-994.

[2]. Mokhtarzade, M., & Zoej, M. V. (2007). Road detection from high-resolution satellite images using Artificial Neural Networks. International Journal of Applied Earth Observation and Geoinformation, 9(1), 32-40.

[3]. Peak, J. E., & Tag, P. M. (1992). Toward automated interpretation of satellite imagery for navy shipboard applications. Bulletin of the American Meteorological Society, 73(7), 995-1008.

[4]. Rao, A., Goyal, D., & Khandelwal, V. (2014). Image Transformation and DWT based Image Decomposition for Covert Communication. Journal of Advanced Computing and Communication Technologies, 2(5), 5- 10.

[5]. Reed, R., & Marks, R. J. (1999). Neural Smithing: Supervised Learning in Feed Forward Artificial Neural Networks. MIT Press.

[6]. Richard, J. A. (1993). Remote Sensing Digital Image Analysis: Introduction, Second Edition. Springer, Newyork.

[7]. Trinder, J., & Li, H. (1999). Automatic Extraction of Man-made Objects from Aerial and Space Images. Brikhauser, Basel,105-114, 1995.

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