Volume XLII-1/W1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 455-459, 2017
https://doi.org/10.5194/isprs-archives-XLII-1-W1-455-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1/W1, 455-459, 2017
https://doi.org/10.5194/isprs-archives-XLII-1-W1-455-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

  31 May 2017

31 May 2017

APPLICATION OF SOFTMAX REGRESSION AND ITS VALIDATION FOR SPECTRAL-BASED LAND COVER MAPPING

J. Wolfe1, X. Jin1, T. Bahr2, and N. Holzer2 J. Wolfe et al.
  • 1Harris Corporation, Broomfield, Colorado, USA
  • 2Harris Corporation, Gilching, Germany

Keywords: Softmax Regression, Classification, Land Cover, Multispectral, Hyperspectral, Desktop, Enterprise

Abstract. The presented Softmax Regression classifier is a generalization of logistic regression. It is used for multi-class classification, where classes are mutually exclusive. Implemented in a classification framework, it provides a flexible approach to customize a classification process. Traditional classification is focused with classifiers that can only be applied on the same dataset. The Softmax Regression classifier can be created and trained on a reference dataset using spectral and spatial information and then applied to similar data multiple times. We present the general workflow of Softmax Regression classification as part of a case study that is based on attribute images derived from hyperspectral airborne and elevation imagery.