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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/21330
Title: Classification of LISS IV imagery using decision tree methods
Authors: Verma A.K.
Garg, Pradeep Kumar
Hari Prasad, Kanchan S.
Dadhwal V.K.
Published in: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
23rd International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Congress, ISPRS 2016
Abstract: Image classification is a compulsory step in any remote sensing research. Classification uses the spectral information represented by the digital numbers in one or more spectral bands and attempts to classify each individual pixel based on this spectral information. Crop classification is the main concern of remote sensing applications for developing sustainable agriculture system. Vegetation indices computed from satellite images gives a good indication of the presence of vegetation. It is an indicator that describes the greenness, density and health of vegetation. Texture is also an important characteristics which is used to identifying objects or region of interest is an image. This paper illustrate the use of decision tree method to classify the land in to crop land and non-crop land and to classify different crops. In this paper we evaluate the possibility of crop classification using an integrated approach methods based on texture property with different vegetation indices for single date LISS IV sensor 5.8 meter high spatial resolution data. Eleven vegetation indices (NDVI, DVI, GEMI, GNDVI, MSAVI2, NDWI, NG, NR, NNIR, OSAVI and VI green) has been generated using green, red and NIR band and then image is classified using decision tree method. The other approach is used integration of texture feature (mean, variance, kurtosis and skewness) with these vegetation indices. A comparison has been done between these two methods. The results indicate that inclusion of textural feature with vegetation indices can be effectively implemented to produce classified maps with 8.33% higher accuracy for Indian satellite IRS-P6, LISS IV sensor images.
Citation: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives (2016), 41: 1061-1066
URI: https://doi.org/10.5194/isprsarchives-XLI-B8-1061-2016
http://repository.iitr.ac.in/handle/123456789/21330
Issue Date: 2016
Publisher: International Society for Photogrammetry and Remote Sensing
Keywords: Classification
Crop
Decision tree
LISS IV
Texture
Vegetation indices
ISSN: 16821750
Author Scopus IDs: 55574182552
57190174828
6506799688
7004389152
Author Affiliations: Verma, A.K., Geomatics Engineering Group, IIT Roorkee, Roorkee, 247667, India
Garg, P.K., Civil Engineering Department, IIT Roorkee, Roorkee, 247667, India
Prasad, K.S.H., Civil Engineering Department, IIT Roorkee, Roorkee, 247667, India
Dadhwal, V.K., National Remote Sensing Centre, ISRO, Hyderabad, 500042, India
Funding Details: The authors are thankful to Indian Institute of Technology Roorkee for providing software's for the study (ENVI 5.1, ARCGIS 10.2.1, MATLAB 2015a and JUNO GPS) and the farmers for providing necessary information and support during the field visit. Indian Institute of Technology Roorkee, IITR: ARCGIS 10.2.1, ENVI 5.1
Corresponding Author: Verma, A.K.; Geomatics Engineering Group, India; email: amit768@gmail.com
Appears in Collections:Conference Publications [CE]

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