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Object Oriented Classification of High Resolution Data for Inventory of Horticultural Crops : Volume Xl-8, Issue 1 (28/11/2014)

By Hebbar, R.

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Book Id: WPLBN0004016289
Format Type: PDF Article :
File Size: Pages 5
Reproduction Date: 2015

Title: Object Oriented Classification of High Resolution Data for Inventory of Horticultural Crops : Volume Xl-8, Issue 1 (28/11/2014)  
Author: Hebbar, R.
Volume: Vol. XL-8, Issue 1
Language: English
Subject: Science, Isprs, International
Collections: Periodicals: Journal and Magazine Collection, Copernicus Publications
Historic
Publication Date:
2014
Publisher: Copernicus Publications, Göttingen, Germany
Member Page: Copernicus Publications

Citation

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Ravishankar, H. M., Trivedi, S., Subramoniam, S. R., Uday, R., Hebbar, R., & Dadhwal, V. K. (2014). Object Oriented Classification of High Resolution Data for Inventory of Horticultural Crops : Volume Xl-8, Issue 1 (28/11/2014). Retrieved from http://www.ebooklibrary.org/


Description
Description: Regional Remote Sensing Centre, NRSC/ISRO, ISITE campus, Marathahalli, Bengaluru, 560037, India. 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.

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

 

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