International Research journal of Management Science and Technology

  ISSN 2250 - 1959 (online) ISSN 2348 - 9367 (Print) New DOI : 10.32804/IRJMST

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IMPERVIOUS SURFACE QUANTIFICATION IN YAMUNA FLOOD PLAIN OF DELHI USING ARTIFICIAL INTELLIGENCE, OBJECT BASED IMAGE ANALYSIS AND STATISTICAL CLASSIFICATION FROM MULTI-SENSOR DATA

    2 Author(s):  MANOJ PANT, SAUMITRA MUKHERJEE

Vol -  8, Issue- 6 ,         Page(s) : 43 - 54  (2017 ) DOI : https://doi.org/10.32804/IRJMST

Abstract

To understand the impact of anthropogenic activities it is important to conduct a detailed estimate of land cover change. A multi-decadal study using temporal datasets from Landsat, IRS supported by high resolution Quickbird images and ground truthing were conducted to detect change specially in the built classes. A temporal coverage land cover change of River Yamuna Flood plain between 1980 till 2015 with a focus to mapchange in Impervious surfaces was conducted. Image classification using Artificial Neural Network, Object based image analysis and traditional statistical classifier was conducted and compared. All classifier used produced good result, however, ANN produced most accurate classification (K = 0.85) compared to OBIA (K=0.81) and MLC (K= 0.79) was marginally behind.

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