Object Based Analysis of Remote Sensing Images

Authors

  • Swasti Patel Ph.D scholar, Department of Computer Engineering, Parul Institute of Engineering and Technology, Parul University, Vadodara, Gujrat, India Author
  • Priya Swaminarayan Ph.D scholar, Department of Computer Engineering, Parul Institute of Computer Application, Parul University, Vadodara, Gujrat, India Author

DOI:

https://doi.org/10.55524/

Keywords:

Image processing, Image Analysis, Landsat, Objects, OBIA and GEOBIA, Remote Sensing

Abstract

 Remote sensing is the process of detecting  and identifying a region's physical properties by assessing  reflected but also transmitted radiation from a range.  Analog pictures include aerial photography, whereas  digital images include satellite images taken using  electronic sensors. A digital picture is made up of pixels  that are arranged in a two-dimensional array. Pixel-based  categorization algorithms were less successful as spatial  resolution increased, since the connection among pixel size  as well as the dimension of observable elements on the  Planet's surface altered dramatically. In this paper the  author discussed about image analysis based on objects  and also characterized OBIA and GOBIA formation and  its analysis. The chief objective of this paper is to gives a  brief overview the technology that has been proved very  efficient in analyzing the satellite (VHR) images. The  study's future scope is as follows: The globe has seen  extraordinary and quick progress in the fields of remote  sensing, geospatial data collecting, and mapping during the  previous several decades. The technology is gaining  traction in terms of its use and application in several  sectors. 

Downloads

Download data is not yet available.

References

T. Veljanovski, U. Kanjir, and K. Oštir, “Object-based image analysis of remote sensing data,” Geod. Vestn., 2011, doi: 10.15292/geodetski-vestnik.2011.04.665-688.

T. Blaschke et al., “Geographic Object-Based Image Analysis - Towards a new paradigm,” ISPRS J. Photogramm. Remote Sens., 2014, doi: 10.1016/j.isprsjprs.2013.09.014.

I. SenGupta, A. Kumar, and R. K. Dwivedi, “Performance Evaluation of Kernel-Based Supervised Noise Clustering Approach,” J. Indian Soc. Remote Sens., 2019, doi: 10.1007/s12524-019-00938-2.

A. Rastogi, R. Singh, R. Sharma, and S. D. Kalony, “The survey of digital image analysis with artificial intelligence DCNN technique,” 2020, doi: 10.1109/SMART50582.2020.9337062.

M. Shahnawaz and R. K. Dwivedi, “Performance analysis of hybrid & non-hybrid approaches in digital image analysis,” 2017, doi: 10.1109/CCAA.2017.8229948.

G. Khan, K. K. Gola, and M. Dhingra, “Efficient techniques for data aggregation in underwater sensor networks,” J. Electr. Syst., 2020.

H. Y. Gu, H. T. Li, L. Yan, and X. J. Lu, “A framework for Geographic Object-Based Image Analysis (GEOBIA) based on geographic ontology,” 2015, doi: 10.5194/isprsarchives-XL-7-W4-27-2015.

M. Janalipour and A. Mohammadzadeh, “Building Damage Detection Using Object-Based Image Analysis and ANFIS from High-Resolution Image (Case Study: BAM Earthquake, Iran),” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2016, doi: 10.1109/JSTARS.2015.2458582.

Q. Zhan, M. Molenaar, and Y. Xiao, “Hierarchical object based image analysis of high-resolution imagery for urban land use classification,” 2001, doi: 10.1109/DFUA.2001.985721.

J. D. Luscier, W. L. Thompson, J. M. Wilson, B. E. Gorham, and L. D. Dragut, “Using digital photographs and object-based image analysis to estimate percent ground cover in vegetation plots,” Front. Ecol. Environ., 2006, doi: 10.1890/1540-9295(2006)4[408:UDPAOI]2.0.CO;2.

T. R. Martha, N. Kerle, C. J. Van Westen, V. Jetten, and K. V. Kumar, “Segment optimization and data-driven thresholding for knowledge-based landslide detection by object-based image analysis,” 2011, doi: 10.1109/TGRS.2011.2151866.

D. Tiede, S. Lang, P. Füreder, D. Hölbling, C. Hoffmann, and P. Zeil, “Automated damage indication for rapid geospatial reporting,” Photogramm. Eng. Remote Sensing, 2011, doi: 10.14358/PERS.77.9.933.

W. Yu, W. Zhou, Y. Qian, and J. Yan, “A new approach for land cover classification and change analysis: Integrating backdating and an object-based method,” Remote Sens. Environ., 2016, doi: 10.1016/j.rse.2016.02.030.

M. A. A. Hoque, S. Phinn, C. Roelfsema, and I. Childs, “Assessing tropical cyclone impacts using object-based moderate spatial resolution image analysis: a case study in Bangladesh,” Int. J. Remote Sens., 2016, doi: 10.1080/01431161.2016.1239286.

K. V. Mitkari, M. K. Arora, and R. K. Tiwari, “Extraction of Glacial Lakes in Gangotri Glacier Using Object-Based Image Analysis,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2017, doi: 10.1109/JSTARS.2017.2727506.

“OBIA – Object-Based Image Analysis (GEOBIA).” https://gisgeography.com/obia-object-based-image analysis-geobia/.

Downloads

Published

2022-03-30

Issue

Vol. 10 No. 2 (2022): International Journal of Innovative Research in Computer Science and Technology (Mar ) 2022

Section

Articles

How to Cite

Object Based Analysis of Remote Sensing Images . (2022). International Journal of Innovative Research in Computer Science & Technology, 10(2), 473–477. https://doi.org/10.55524/
Crossref
Scopus
Google Scholar
Europe PMC