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A Hybrid CNN + Random Forest Approach to Delineate Debris Covered Glaciers Using Deep Features

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Abstract

The main aim of this study is to propose a novel hybrid deep learning framework approach for accurate mapping of debris covered glaciers. The framework comprises of integration of several CNNs architecture, in which different combinations of Landsat 8 multispectral bands (including thermal band), topographic and texture parameters are passed as input for feature extraction. The output of an ensemble of these CNNs is hybrid with random forest model for classification. The major pillars of the framework include: (1) technique for implementing topographic and atmospheric corrections (preprocessing), (2) the proposed hybrid of ensemble of CNNs and random forest classifier, and (3) procedures to determine whether a pixel predicted as snow is a cloud edge/shadow (post-processing). The proposed approach was implemented on the multispectral Landsat 8 OLI (operational land imager)/TIRS (thermal infrared sensor) data and Shuttle Radar Topography Mission Digital Elevation Model for the part of the region situated in Alaknanda basin, Uttarakhand, Himalaya. The proposed framework was observed to outperform (accuracy 96.79%) the current state-of-art machine learning algorithms such as artificial neural network, support vector machine, and random forest. Accuracy assessment was performed by means of several statistics measures (precision, accuracy, recall, and specificity).

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Authors and Affiliations

  1. Department of Earthquake Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India

    Rahul Nijhawan & Josodhir Das

  2. Department of Computer Science and Engineering, Indian Institute of Technology Roorkee, Roorkee, 247667, India

    Raman Balasubramanian

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  1. Rahul Nijhawan
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  2. Josodhir Das
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  3. Raman Balasubramanian
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Correspondence to Rahul Nijhawan.

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Nijhawan, R., Das, J. & Balasubramanian, R. A Hybrid CNN + Random Forest Approach to Delineate Debris Covered Glaciers Using Deep Features. J Indian Soc Remote Sens 46, 981–989 (2018). https://doi.org/10.1007/s12524-018-0750-x

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  • DOI: https://doi.org/10.1007/s12524-018-0750-x

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