Salmon, BPKleynhans, WSchwegmann, CPOlivier, JC2016-03-042016-03-042015-07Salmon, BP, Kleynhans, W, Schwegmann, CP and Olivier, JC. 2015. Proper comparison among methods using a confusion matrix. In: IGARSS 2015, Milan, Italy, 26-31 July 2015http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7326461http://hdl.handle.net/10204/8464IGARSS 2015, Milan, Italy, 26-31 July 2015. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website.An important aspect of research in the remote sensing field is to objectively compare different classifiers. This is the foundation of hundreds of research projects and in this paper we will address some raising concerns when evaluating solutions for classification of data sets with skewed class distributions. The quality of assessment is based on the problem specified by the user and the corresponding hypothesis defined. This hypothesis will determine how two or more classifiers are scored to determine which one is better for a particular application. In this paper we present two experiments that illustrate how, if unaware and misunderstood, statistical measurements can be misleading. One experiment is based on a Synthetic Aperture Radar image with a highly skewed class distribution and the second experiment is based on a Landsat image with a minor skewed distribution. From both experiments it can be seen that ill-defining the problem, can lead to false statements and the reporting of statistically invalid conclusions.enImage classificationProbability distributionRemote sensingSatellitesConference PresentationSalmon, B., Kleynhans, W., Schwegmann, C., & Olivier, J. (2015). Proper comparison among methods using a confusion matrix. IEEE. http://hdl.handle.net/10204/8464Salmon, BP, W Kleynhans, CP Schwegmann, and JC Olivier. "Proper comparison among methods using a confusion matrix." (2015): http://hdl.handle.net/10204/8464Salmon B, Kleynhans W, Schwegmann C, Olivier J, Proper comparison among methods using a confusion matrix; IEEE; 2015. http://hdl.handle.net/10204/8464 .TY - Conference Presentation AU - Salmon, BP AU - Kleynhans, W AU - Schwegmann, CP AU - Olivier, JC AB - An important aspect of research in the remote sensing field is to objectively compare different classifiers. This is the foundation of hundreds of research projects and in this paper we will address some raising concerns when evaluating solutions for classification of data sets with skewed class distributions. The quality of assessment is based on the problem specified by the user and the corresponding hypothesis defined. This hypothesis will determine how two or more classifiers are scored to determine which one is better for a particular application. In this paper we present two experiments that illustrate how, if unaware and misunderstood, statistical measurements can be misleading. One experiment is based on a Synthetic Aperture Radar image with a highly skewed class distribution and the second experiment is based on a Landsat image with a minor skewed distribution. From both experiments it can be seen that ill-defining the problem, can lead to false statements and the reporting of statistically invalid conclusions. DA - 2015-07 DB - ResearchSpace DP - CSIR KW - Image classification KW - Probability distribution KW - Remote sensing KW - Satellites LK - https://researchspace.csir.co.za PY - 2015 T1 - Proper comparison among methods using a confusion matrix TI - Proper comparison among methods using a confusion matrix UR - http://hdl.handle.net/10204/8464 ER -