Advances in Remote Sensing, 2015, 4, 177-195 Published Online September 2015 in SciRes. http://www.scirp.org/journal/ars http://www.scirp.org/Journal/PaperInformation.aspx?doi=10.4236/ars.2015.43015 How to cite this paper: Jawak, S.D., Devliyal, P. and Luis, A.J. (2015) A Comprehensive Review on Pixel Oriented and Object Oriented Methods for Information Extraction from Remotely Sensed Satellite Images with a Special Emphasis on Cryos- pheric Applications. Advances in Remote Sensing, 4, 177-195. http://dx.doi.org/10.4236/ars.2015.43015 A Comprehensive Review on Pixel Oriented and Object Oriented Methods for Information Extraction from Remotely Sensed Satellite Images with a Special Emphasis on Cryospheric Applications Shridhar D. Jawak 1 , Prapti Devliyal 2 , Alvarinho J. Luis 1 1 Earth System Science Organization (ESSO), National Centre for Antarctic & Ocean Research (NCAOR), Ministry of Earth Sciences, Government of India, Vasco da Gama, India 2 Department of Civil Engineering, Symbiosis Institute of Technology, Symbiosis International University, Pune, India Email: shridhar.jawak@ncaor.gov.in , praptid.90@gmail.com , alvluis@ncaor.gov.in Received 22 May 2015; accepted 20 July 2015; published 23 July 2015 Copyright © 2015 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Abstract Image classification is one of the most basic operations of digital image processing. The present review focuses on the strengths and weaknesses of traditional pixel-based classification (PBC) and the advances of object-oriented classification (OOC) algorithms employed for the extraction of in- formation from remotely sensed satellite imageries. The state-of-the-art classifiers are reviewed for their potential usage in urban remote sensing (RS), with a special focus on cryospheric appli- cations. Generally, classifiers for information extraction can be divided into three catalogues: 1) based on the type of learning (supervised and unsupervised), 2) based on assumptions on data distribution (parametric and non-parametric) and, 3) based on the number of outputs for each spatial unit (hard and soft). The classification methods are broadly based on the PBC or the OOC approaches. Both methods have their own advantages and disadvantages depending upon their area of application and most importantly the RS datasets that are used for information extraction. Classification algorithms are variedly explored in the cryosphere for extracting geospatial infor- mation for various logistic and scientific applications, such as to understand temporal changes in geographical phenomena. Information extraction in cryospheric regions is challenging, accounting to the very similar and conflicting spectral responses of the features present in the region. The spectral responses of snow and ice, water, and blue ice, rock and shadow are a big challenge for the pixel-based classifiers. Thus, in such cases, OOC approach is superior for extracting informa- tion from the cryospheric regions. Also, ensemble classifiers and customized spectral index ratios