Volume XLII-5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-5, 703-706, 2018
https://doi.org/10.5194/isprs-archives-XLII-5-703-2018
© 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, 703-706, 2018
https://doi.org/10.5194/isprs-archives-XLII-5-703-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  19 Nov 2018

19 Nov 2018

LAND USE AND LAND COVER CLASSIFICATION OF MULTISPECTRAL LANDSAT-8 SATELLITE IMAGERY USING DISCRETE WAVELET TRANSFORM

J. Sharma, R. Prasad, V. N. Mishra, V. P. Yadav, and R. Bala J. Sharma et al.
  • Department of Physics, Indian Institute of Technology (BHU), Varanasi, India

Keywords: LULC, LANDSAT-8, DWT, Minimum distance classifier, Kappa coefficient

Abstract. Land use and land cover (LULC) classification of satellite imagery is an important research area and studied exclusively in remote sensing. However, accurate and appropriate land use/cover detection is still a challenge. This paper presents a wavelet transform based LULC classification using Landsat 8-OLI data. The study area for the present work is a small part of Varanasi district, Uttar Pradesh, India. The atmospheric correction of the image was performed using Quick Atmospheric Correction (QUAC) method. The image was decomposed into its approximation and detail coefficients up to eight levels using discrete wavelet transform (DWT) method. The approximation images were layer stacked with the original image. The minimum distance classifier was used for classifying the image into six LULC classes namely water, agriculture, urban, fallow land, sand, and vegetation. The classification accuracy for all decomposition levels was compared with that of classified product based on original multispectral image. The classification accuracy for multi-spectral image was found to be 75.27%. Whereas, the classification accuracies were found to improve up to 85.97%, 88.87%, 93.47%, 95.03%, 93.01, 92.32% and 90.80% for second, third, fourth, fifth, six, seventh and eight level decomposition, respectively. The significantly improved accuracy was found for images decomposed at level five. Thus, the approach of DWT for LULC classification can be used to increase the classification accuracy significantly.