4 January 2018 Proposed hybrid-classifier ensemble algorithm to map snow cover area
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Abstract
Metaclassification ensemble approach is known to improve the prediction performance of snow-covered area. The methodology adopted in this case is based on neural network along with four state-of-art machine learning algorithms: support vector machine, artificial neural networks, spectral angle mapper, K -mean clustering, and a snow index: normalized difference snow index. An AdaBoost ensemble algorithm related to decision tree for snow-cover mapping is also proposed. According to available literature, these methods have been rarely used for snow-cover mapping. Employing the above techniques, a study was conducted for Raktavarn and Chaturangi Bamak glaciers, Uttarakhand, Himalaya using multispectral Landsat 7 ETM+ (enhanced thematic mapper) image. The study also compares the results with those obtained from statistical combination methods (majority rule and belief functions) and accuracies of individual classifiers. Accuracy assessment is performed by computing the quantity and allocation disagreement, analyzing statistic measures (accuracy, precision, specificity, AUC, and sensitivity) and receiver operating characteristic curves. A total of 225 combinations of parameters for individual classifiers were trained and tested on the dataset and results were compared with the proposed approach. It was observed that the proposed methodology produced the highest classification accuracy (95.21%), close to (94.01%) that was produced by the proposed AdaBoost ensemble algorithm. From the sets of observations, it was concluded that the ensemble of classifiers produced better results compared to individual classifiers.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Rahul Nijhawan, Rahul Nijhawan, Balasubramanian Raman, Balasubramanian Raman, Josodhir Das, Josodhir Das, } "Proposed hybrid-classifier ensemble algorithm to map snow cover area," Journal of Applied Remote Sensing 12(1), 016003 (4 January 2018). https://doi.org/10.1117/1.JRS.12.016003 . Submission: Received: 23 May 2017; Accepted: 7 December 2017
Received: 23 May 2017; Accepted: 7 December 2017; Published: 4 January 2018
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