Volume XLII-5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5, 665-670, 2018
https://doi.org/10.5194/isprs-archives-XLII-5-665-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5, 665-670, 2018
https://doi.org/10.5194/isprs-archives-XLII-5-665-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Nov 2018

19 Nov 2018

A COMPARATIVE STUDY OF ADVANCED LAND USE/LAND COVER CLASSIFICATION ALGORITHMS USING SENTINEL-2 DATA

K. Nivedita Priyadarshini1, M. Kumar2, S. A. Rahaman1, and S. Nitheshnirmal1 K. Nivedita Priyadarshini et al.
  • 1Dept. of Geography, Bharathidasan University, Tamil Nadu, India
  • 2Indian Institute of Remote Sensing (ISRO), Dehradun, Uttarakhand, India

Keywords: Land Use/Land Cover, Classification Algorithms, Sentinel-2A, Supervised, Unsupervised, Accuracy Assessment

Abstract. Land Use/ Land Cover (LU/LC) is a major driving phenomenon of distributed ecosystems and its functioning. Interpretation of remote sensor data acquired from satellites requires enhancement through classification in order to attain better results. Classification of satellite products provides detailed information about the existing landscape that can also be analyzed on temporal basis. Image processing techniques acts as a platform for analysis of raw data using supervised and unsupervised classification algorithms. Classification comprises two broad ranges in which, the analyst specifies the classes by defining the training sites called supervised classification where as automatically clustering of pixels to the defined number of classes namely the unsupervised classification. This study attempts to perform the LU/LC classification for Paonta Sahib region of Himachal Pradesh which is a major industrial belt. The data obtained from Sentinel 2A, from which the stacked bands of 10m resolution are only used. Various classification algorithms such as Minimum Distance, Maximum Likelihood, Parallelepiped and Support Vector Machine (SVM) of supervised classifiers and ISO Data, K-Means of unsupervised classifiers are applied. Using the applied classification results, accuracy assessment is estimated and compared. Of these applied methods, the classification method, maximum likelihood provides highest accuracy and is considered to be the best for LU/LC classification using Sentinel-2A data.