Volume XLI-B8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 1061-1066, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-1061-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 1061-1066, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-1061-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  24 Jun 2016

24 Jun 2016

CLASSIFICATION OF LISS IV IMAGERY USING DECISION TREE METHODS

Amit Kumar Verma1, P. K. Garg2, K. S. Hari Prasad2, and V. K. Dadhwal3 Amit Kumar Verma et al.
  • 1Geomatics Engineering Group, IIT Roorkee, Roorkee-247667, India
  • 2Civil Engineering Department, IIT Roorkee, Roorkee-247667, India
  • 3National Remote Sensing Centre, ISRO, Hyderabad-500042, India

Keywords: Crop, Vegetation Indices, Texture, Decision Tree, Classification, LISS IV

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 classifiedmaps with 8.33% higher accuracy for Indian satellite IRS-P6, LISS IV sensor images.