Object oriented classification of high resolution data for inventory of horticultural crops

Other literature type English OPEN
Hebbar, R. ; Ravishankar, H. M. ; Trivedi, S ; Subramoniam, S. R. ; Uday, R. ; Dadhwal, V. K. (2014)

High resolution satellite images are associated with large variance and thus, per pixel classifiers often result in poor accuracy especially in delineation of horticultural crops. In this context, object oriented techniques are powerful and promising methods for classification. In the present study, a semi-automatic object oriented feature extraction model has been used for delineation of horticultural fruit and plantation crops using Erdas Objective Imagine. Multi-resolution data from Resourcesat LISS-IV and Cartosat-1 have been used as source data in the feature extraction model. Spectral and textural information along with NDVI were used as inputs for generation of Spectral Feature Probability (SFP) layers using sample training pixels. The SFP layers were then converted into raster objects using threshold and clump function resulting in pixel probability layer. A set of raster and vector operators was employed in the subsequent steps for generating thematic layer in the vector format. This semi-automatic feature extraction model was employed for classification of major fruit and plantations crops viz., mango, banana, citrus, coffee and coconut grown under different agro-climatic conditions. In general, the classification accuracy of about 75–80 per cent was achieved for these crops using object based classification alone and the same was further improved using minimal visual editing of misclassified areas. A comparison of on-screen visual interpretation with object oriented approach showed good agreement. It was observed that old and mature plantations were classified more accurately while young and recently planted ones (3 years or less) showed poor classification accuracy due to mixed spectral signature, wider spacing and poor stands of plantations. The results indicated the potential use of object oriented approach for classification of high resolution data for delineation of horticultural fruit and plantation crops. The present methodology is applicable at local levels and future development is focused on up-scaling the methodology for generation of fruit and plantation crop maps at regional and national level which is important for creation of database for overall horticultural crop development.
  • References (9)

    IHD, 2013. Indian Horticulture Database (2013), National Horticulture Board, Min. of Agriculture, pp.289.

    Kasper Johansen, Stuart Phinn, Christian Witte, Seonaid Philip, and Lisa Newton, 2009, Mapping Banana Plantations from Object-oriented Classification of SPOT-5 Imagery. PE & RS, Vol. 75, No. 9, pp. 1069-1081.

    Mueller, M., K. Segl, and H. Kaufmann, 2004. Edge- and region-based segmentation technique for the extraction of large, man-made objects in high-resolution satellite imagery, Pattern Recognition, 37:1619-1628.

    Murthy, C.S., Sesha Sai, M.V.R., Bhanuja Kumari, V. and Roy, P.S., 2007, Agricultural drought assessment at disaggregated level using AWiFS /WiFS data of Indian Remote sensing satellites, Geocarto Int., 22, 127-140.

    NHRDF, 2012, Annual Report, National Horticulture Research and Development Foundation, Nashik.pp.108.

    RRSC-South, 2012, Geospatial technique for inventory of rubber plantations and identification of potential waste lands in Tripura state. Project report. RRSC/NRSC, www.nrsc.gov.in/pdf/prubber.pdf.

    Srivastava, R.J., and J.L. Gebelein, 2007. Land cover classification and economic assessment of citrus groves using remote sensing, ISPRS Journal of Photogrammetry and Remote Sensing, 61:341-353.

    Tenkabail P.S., Rao B.M., Pal P.K., and Das H.P., 2004, The use of remote sensing data for agricultural drought assessment and monitoring in Southwest Asia, International Water Management Institute (IWMI) Research report No. 85, Srilanka.

    Nageswara Rao, K.S. Ramesh, and S. Elango, 2002. Acreage and production, estimation of mango orchards using Indian Remote Sensing (IRS) satellite data, Scientia Horticulturae, 93:105-123.

  • Metrics
    No metrics available
Share - Bookmark