EFFICIENT SCHEMES OF CLASSIFIERS FOR REMOTE SENSING SATELLITE IMAGERIES OF LAND USE PATTERN CLASSIFICATIONS

Abstract

Land use pattern classification of remote sensing imagery data is imperative to research that is used in remote sensing applications. Remote sensing (RS) technologies were exploited to mine some of the significant spatially variable factors, such as land cover and land use (LCLU), from satellite images of remote arid areas in Karnataka, India. Four diverse classification techniques unsupervised, and supervised (Maximum likelihood, Mahalnobis Distance, and Minimum Distance) are applied in Bellary district in Karnataka, India for the classification of the raw satellite images. The developed maps are then visually compared with each  other and accuracy evaluations make using of ground-truths are carried out. It was initiated that the Maximum likelihood technique gave the finest results and both Minimum distance and Mahalnobis distance methods overvalued agricultural land areas. In spite of missing a few insignificant features due to the low resolution of the satellite images, a high-quality accord between parameters extracted automatically from the developed maps and field observations was found.

Keywords: Remote sensing (RS), land cover and land use (LCLU).

Author Biographies

Hemamalini H C, - Bangalore Regional Centre, Bangalore, India.

Assistant Regional Director,IGNOU Regional Center,Bangalore

Prabhuraj D K, University of Agricultural Sciences, Bangalore, India.

Dr.D.K.Prabhuraj
Director, KSRSAC(Karnataka State Remote Sensing Applications Centre)
DPAR(e-Governance), Govt.of Karnataka

References

Bruzzone, L., Cossu, R. and Vernazza, G. 2002. Combining parametric and non-parametric algorithms for partially unsupervised classification of multitemporal remote sensing images. Information Fusion, 3: 289-297.
Goel, P. K., Prasher, S. O., Landry, J. A., Patel, R. M. and Viau, A. A., 2003. Hyperspectral image classification to detect weed infestations and nitrogen status in corn. Transactions of Americans Society of Agricultural and Biological Engineers,46(2): 539-550.
Graney, M. J., and Engle, V. F., 2000. Stability of performance of activities of daily living using the MDS.Stat Med., 19(14): 1889-99.
Hiremath, P. S., and Kodge, B. G., 2010. Visualization techniques for data mining of Latur district satellite imagery. Advances in computational research, 2 (1): 21-24.
Kun Wang and Youchuan Wan, 2009. Classification of remote sensing images using Fuzzy multi-classifiers.Yangze River Scientific Research Institute, 978(1): 4244-4738.
Lu, D., Weng, Q., 2007. A survey of image classification methods and techniques for improving classification performance". International Journal of Remote Sensing, 28(5): 823-870.
Nageshwara R., and Vaidyanadhan, R.,1981. LandUse Capability Studies from Aerial Photo- Interpretation-A Case Study from Krishna Delta, India. Geographical Review of India, 3:226-236.
Patil, S.S., Sachidananda, Angadi, U.B., Prabhuraj, D.K.,2014, Machine Learning Technique Approaches Versus Statistical Methods in Classification of Multispectral Remote Sensing Data Using Maximum Likelihood Classification: Koluru Hobli, Bellary Taluk, District, Karnataka State, INDIA. International of Advanced Remote Sensing and GIS, 3(1):525-531.
Perumal, K. and Bhaskaran, R., 2010. Supervised classification performance of multispectral images, Journal of Computing, 2(2): 2151-9617.
Nageswara Rao K and Vaidyanathan R (1981). Land use capability studies from aerial photo interpretation — A case study from Krishna Delta, India. Geog. Rev. of India, 43(3).
Satio, H., Bellan, M. F., Al-Habshi, A., Aizpuru, M. and Blasco, F., 2003.Mangrove research and coastal ecosystem studies with SPOT-4 HRVIR and terra aster in the Arabian Gulf. 24(21): 4073 – 4092.
Shanmugam, S., Lucas, N., Phipps, P., Richards. A., and Barnsley, M., 2003. Assessment of remote sensing techniques for habitat mapping in coastal dune ecosystems, Journal of Coastal Research, 19(1): 64-75.
Sheldon MR, Fillyaw MJ, Thompson WD.,1996. The use and interpretation of the Friedman test in the analysis of ordinal-scale data in repeated measures designs. Physiother Res Int, 1(4):221-8
Published
12/02/2024
How to Cite
UDUPI, Sachidananda K et al. EFFICIENT SCHEMES OF CLASSIFIERS FOR REMOTE SENSING SATELLITE IMAGERIES OF LAND USE PATTERN CLASSIFICATIONS. Mercator, Fortaleza, v. 23, feb. 2024. ISSN 1984-2201. Available at: <http://www.mercator.ufc.br/mercator/article/view/e23004en>. Date accessed: 21 apr. 2024. doi: https://doi.org/10.4215/rm2024.e23004.
Section
ARTICLES