Volume XLII-1/W1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 607-612, 2017
https://doi.org/10.5194/isprs-archives-XLII-1-W1-607-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 607-612, 2017
https://doi.org/10.5194/isprs-archives-XLII-1-W1-607-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  31 May 2017

31 May 2017

LANDSAT-8 OPERATIONAL LAND IMAGER CHANGE DETECTION ANALYSIS

W. Pervez, S. A. Khan, E. Hussain, F. Amir, and M. A. Maud W. Pervez et al.
  • National University of Sciences and Technology Islamabad, Pakistan

Keywords: Change Detection Analysis, Satellite Image Processing, Remote Sensing, Imaging Sciences, Operational Land Imager

Abstract. This paper investigated the potential utility of Landsat-8 Operational Land Imager (OLI) for change detection analysis and mapping application because of its superior technical design to previous Landsat series. The OLI SVM classified data was successfully classified with regard to all six test classes (i.e., bare land, built-up land, mixed trees, bushes, dam water and channel water). OLI support vector machine (SVM) classified data for the four seasons (i.e., spring, autumn, winter, and summer) was used to change detection results of six cases: (1) winter to spring which resulted reduction in dam water mapping and increases of bushes; (2) winter to summer which resulted reduction in dam water mapping and increase of vegetation; (3) winter to autumn which resulted increase in dam water mapping; (4) spring to summer which resulted reduction of vegetation and shallow water; (5) spring to autumn which resulted decrease of vegetation; and (6) summer to autumn which resulted increase of bushes and vegetation . OLI SVM classified data resulted higher overall accuracy and kappa coefficient and thus found suitable for change detection analysis.