Object oriented classification of high resolution data for inventory of horticultural crops
Other literature type
Ravishankar, H. M.
Subramoniam, S. R.
Dadhwal, V. K.
(issn: 2194-9034, eissn: 2194-9034)
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