M
Manoj K. Arora
Researcher at Indian Institute of Technology Roorkee
Publications - 108
Citations - 5619
Manoj K. Arora is an academic researcher from Indian Institute of Technology Roorkee. The author has contributed to research in topics: Hyperspectral imaging & Landslide. The author has an hindex of 34, co-authored 106 publications receiving 4841 citations. Previous affiliations of Manoj K. Arora include Syracuse University & PEC University of Technology.
Papers
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A comparative study of conventional, ANN black box, fuzzy and combined neural and fuzzy weighting procedures for landslide susceptibility zonation in Darjeeling Himalayas
TL;DR: In this paper, apart from conventional weighting system, objective weight assignment procedures based on techniques such as artificial neural network (ANN), fuzzy set theory and combined neural and fuzzy set theories have been assessed for preparation of LSZ maps in a part of the Darjeeling Himalayas.
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Decision tree regression for soft classification of remote sensing data
TL;DR: A decision tree regression approach is employed to determine class proportions within a pixel so as to produce soft classification from remote sensing data, compared with those achieved by the most widely used maximum likelihood classifier, implemented in the soft mode, and a supervised version of the fuzzy c-means classifier.
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GIS-based Landslide Hazard Zonation in the Bhagirathi (Ganga) Valley, Himalayas
TL;DR: In this paper, a part of the Bhagirathi valley in the Garhwal Himalaya was selected for landslide hazard zonation using different types of data including Survey of India topographic maps, geological (lithological and structural) maps, IRS-1B and-1D multispectral and PAN satellite sensor data and field observations.
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An approach for GIS-based statistical landslide susceptibility zonation—with a case study in the Himalayas
TL;DR: In this article, two methods, the Information Value (InfoVal) and the Landslide Nominal Susceptibility Factor (LNSF) methods that are based on bivariate statistical analysis have been applied for LSZ mapping in a part of the Himalayas.
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An evaluation of some factors affecting the accuracy of classification by an artificial neural network
Giles M. Foody,Manoj K. Arora +1 more
TL;DR: The effect of four factors on the accuracy with which agricultural crops may be classified from airborne thematic mapper (ATM) data was investigated and a log-linear modelling approach was used to evaluate the simultaneous effect of the factors on classification accuracy.