A robust, cross-validation classification method (RCM) for improved mapping accuracy and confidence metrics

A modified approach to existing classification procedures, the Robust Classification Method (RCM), is introduced in this study. This algorithm is based on a randomized and repeated sampling of a training dataset in concert with traditional cross-validation of the classification results. A series of predictions (classified maps) and associated uncertainty maps and statistics are produced. The algorithm and associated outputs are discussed and a case study dealing with the classification of surficial materials in an area in Nunavut, Canada (NTS mapsheet 66A) using RCM is presented. The RCM was especially useful for assessing the effects of spectral and spatial variability in the classification process. Specifically, this method provided a majority classification and variability map and confusion statistics to bracket uncertainty in the classification process with respect to statistical (spectral) variability in the training dataset used to perform the classification as well as identifying areas that show spatial variability in classification.

[1]  C H Chen.  Frontiers of remote sensing information processing , 2003 .

[2]  Brigitte Leblon,et al.  Surficial materials mapping in Nunavut, Canada with multibeam RADARSAT-2 dual-polarization C-HH and C-HV, LANDSAT-7 ETM+, and DEM data , 2012 .

[3]  A. Jawad,et al.  Computer processing of remotely sensed images , 2005 .

[4]  B. Efron,et al.  A Leisurely Look at the Bootstrap, the Jackknife, and , 1983 .

[5]  H. Jones,et al.  Remote Sensing of Vegetation: Principles, Techniques, and Applications , 2010 .

[6]  Rupert G. Miller.  The jackknife-a review , 1974 .

[7]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[8]  R. Congalton,et al.  Accuracy assessment: a user's perspective , 1986 .

[9]  S. Lanyon.  Jackknifing and Bootstrapping: Important "New" Statistical Techniques for Ornithologists , 1987 .

[10]  John R. Jensen,et al.  Introductory Digital Image Processing: A Remote Sensing Perspective , 1986 .

[11]  Yoav Freund,et al.  A Short Introduction to Boosting , 1999 .

[12]  David Hinkley,et al.  Bootstrap Methods: Another Look at the Jackknife , 2008 .

[13]  B. Datt,et al.  On the relationship between training sample size and data dimensionality: Monte Carlo analysis of broadband multi-temporal classification , 2005 .

[14]  Giles M. Foody,et al.  Increasing soft classification accuracy through the use of an ensemble of classifiers , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[15]  Eric Bauer,et al.  An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.

[16]  Derek M. Rogge,et al.  Mapping lithology in Canada's Arctic: application of hyperspectral data using the minimum noise fraction transformation and matched filtering , 2005 .

[17]  Giles M. Foody,et al.  Increasing soft classification accuracy through the use of an ensemble of classifiers , 2005, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05..

[18]  Timothy A. Warner,et al.  The SAGE Handbook of Remote Sensing , 2009 .

[19]  Kagan Tumer,et al.  Analysis of decision boundaries in linearly combined neural classifiers , 1996, Pattern Recognit..

[20]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[21]  Jim Piper.  The effect of zero feature correlation assumption on maximum likelihood based classification of chromosomes , 1987 .

[22]  Ron Kohavi,et al.  A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection , 1995, IJCAI.

[23]  John A. Richards,et al.  Remote Sensing Digital Image Analysis: An Introduction , 1999 .

[24]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[25]  Adrian F. M. Smith,et al.  A Bayesian CART algorithm , 1998 .

[26]  M. Chavance.  [Jackknife and bootstrap]. , 1992, Revue d'epidemiologie et de sante publique.

[27]  M. Kenward,et al.  An Introduction to the Bootstrap , 2007 .

[28]  Ray Bert,et al.  Book Review: Computer Processing of Remotely-Sensed Images: An Introduction, Third Edition , by Paul M. Mather. Chichester, United Kingdom: John Wiley & Sons Ltd., 2004 , 2004 .