A comparison of selected classification algorithms for mapping bamboo patches in lower Gangetic plains using very high resolution WorldView 2 imagery

Bamboo is used by different communities in India to develop indigenous products, maintain livelihood and sustain life. Indian National Bamboo Mission focuses on evaluation, monitoring and development of bamboo as an important plant resource. Knowledge of spatial distribution of bamboo therefore becomes necessary in this context. The present study attempts to map bamboo patches using very high resolution (VHR) WorldView 2 (WV 2) imagery in parts of South 24 Parganas, West Bengal, India using both pixel and object-based approaches. A combined layer of pan-sharpened multi-spectral (MS) bands, first 3 principal components (PC) of these bands and seven second order texture measures based Gray Level Cooccurrence Matrices (GLCM) of first three PC were used as input variables. For pixel-based image analysis (PBIA), recursive feature elimination (RFE) based feature selection was carried out to identify the most important input variables. Results of the feature selection indicate that the 10 most important variables include PC 1, PC 2 and their GLCM mean along with 6 MS bands. Three different sets of predictor variables (5 and 10 most important variables and all 32 variables) were classified with Support Vector Machine (SVM) and Random Forest (RF) algorithms. Producer accuracy of bamboo was found to be highest when 10 most important variables selected from RFE were classified with SVM (82%). However object-based image analysis (OBIA) achieved higher classification accuracy than PBIA using the same 32 variables, but with less number of training samples. Using object-based SVM classifier, the producer accuracy of bamboo reached 94%. The significance of this study is that the present framework is capable of accurately identifying bamboo patches as well as detecting other tree species in a tropical region with heterogeneous land use land cover (LULC), which could further aid the mandate of National Bamboo Mission and related programs. (C) 2013 Elsevier B.V. All rights reserved.