Automated Extraction of Image Features from Geospatial Imagery in form of Grids and Identification of Images using its Corresponding Grid-id

Ganesh Chirkhare

Ganesh Chirkhare Student Dept. of Computer Science and Engg., G. H. Raisoni Academy of Engg. and Technology, Nagpur, Maharashtra, India

Abstract

This paper advances a new approach to implement spatial modeler
scripting to subset the image using shapefile and searching technique
for image subset, the employed professionals at MRSAC were facing the
problem while performing the process of image sub setting and searching
different subset images. They have to import the grid repeatedly
while perceiving the image and plotting the region which needs to
be clipped. After sub-setting the image, if any mismatch or improper
clipping is observed then the complete process needs to be restarted
from scratch, which would not be preferable while working with large
datasets consisting many different images. To improve this work, flow
an operative method has been designed known as spatial modeler for
doing the task in an effective and sensitive manner. The clients also have
to search for the set of clipped image, the employees at MRSAC search
the folder manually which takes a considerable amount of time, to
minimize this practice a searching mechanism also needs to be designed
and implemented to search the image subset using its unique grid-id

How to cite this article:
Chirkhare G, Jha R, Gadicha V. Automated
Extraction of Image Features from Geospatial
Imagery in form of Grids and Identification of
Images using its Corresponding Grid-id. J Adv
Res Image Proc Appl 2020; 3(1): 14-18.

References

1. Chunyan Y, Song, Meiping & Chang, Chein I. 2018 Band
Subset Selection for Hyperspectral Image Classification.
Remote Sensing. 10. 113. 10.3390/rs10010113.
2. Zheng X, Liu W, Qian Li et al. Research on pattern
recognition methods of TM remote sensing images,
2010.
3. Wenkai Li, Qinghua Guo, Charles Elkan. A Positive
And Unlabeled Learning Algorithm For One-Class
Classification Of Remote-Sensing Data. IEEE Transactions
On Geoscience and Remote Sensing 2011; 49(2).
4. Proceeding of the First Malaysian Remote Sensing
Society Conference on Remote Sensing and GIS, pp.
R1-R9, 1996-Nov.-25-27.
5. Mahmood NN, Loh KF, Ahmad S. Remote sensing
research in Malaysia. Aug-1997.
6. Huk S, Mirza MI, Ahmad S. Monitoring waterlogging
and surface salinity using satellite remote sensing
data Aug-2002.
7. Anger CD, Babey SK, Adamson RJ., A New Approach
to Imaging Spectroscopy, SPIE Proceedings, Imaging
Spectroscopy of the Terrestrial Environment 1990;1298:
72 - 86. -specifically, CASI.1997.
8. Curlander JC. 1991 Synthetic Aperture Radar, Systems
& Signal Processing. John Wiley New York.2015.
9. Lillesand TM, Kiefer RW, Chipman J. Rem. Sens. Image
Interpretation, NY: J .Wiley & Sons,2008.
10. Study on subset size selection in digital image correlation
for speckle patterns,Optics Express 16(10):7037-48 ·
June 2008.
11. Use of Image Subset Features in Image Retrieval with
Self-Organizing Maps Conference: Image and Video
Retrieval: Third International Conference, CIVR 2004,
Dublin, Ireland, July 21-23, 2004