Ensemble methods for automatic masking of clouds in AVIRIS imagery

Charles M. Bachmann, Eugene E. Clothiaux, John W. Moore, Keith J. Andreano, Dong Q. Luong

Research output: Contribution to conferencePaper

4 Scopus citations

Abstract

We describe the first-phase of an investigation into techniques for automatic cloud masking in remote sensing data. BCM Projection Pursuit networks are explored as a method of unsupervised feature extraction from AVIRIS images. Search vectors in this method discover directions in the data in which the projected data is skew or multi-modal, by minimizing a projection index which depends on higher moments of the projected data distribution. Ensemble methods are used to fuse information from extracted BCM features and to smooth the mapping of these features to classification of image pixels. Ensemble hierarchies contain multiple levels of networks, combining BCM at the lowest levels with backward propagation algorithms, based on cross-entropy minimization, at higher levels in the ensembles. Predicted cloud masks are compared against cloud masks derived from human interpretation; ensembles achieve better overall classification accuracy than single BP networks.

Original languageEnglish (US)
Pages394-403
Number of pages10
StatePublished - Dec 1 1994
EventProceedings of the 4th IEEE Workshop on Neural Networks for Signal Processing (NNSP'94) - Ermioni, GREECE
Duration: Sep 6 1994Sep 8 1994

Other

OtherProceedings of the 4th IEEE Workshop on Neural Networks for Signal Processing (NNSP'94)
CityErmioni, GREECE
Period9/6/949/8/94

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Ensemble methods for automatic masking of clouds in AVIRIS imagery'. Together they form a unique fingerprint.

  • Cite this

    Bachmann, C. M., Clothiaux, E. E., Moore, J. W., Andreano, K. J., & Luong, D. Q. (1994). Ensemble methods for automatic masking of clouds in AVIRIS imagery. 394-403. Paper presented at Proceedings of the 4th IEEE Workshop on Neural Networks for Signal Processing (NNSP'94), Ermioni, GREECE, .