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Abstract

Evidence from the recent literature demonstrates little progress in discrete (hard) classification of land cover from remotely sensed imagery. An empirical study is presented to test training procedures with neural networks for soft (mixture) classification. The results show that land cover mixtures are best recognized following training with two component mixed pixels, and that linearly rescaled or binned target vector representations are equally satisfactory.

Additional information

Authors: BERNARD A C, JRC Ispra (IT);WILKINSON G G, JRC Ispra (IT);KANELLOPOULOS I, JRC Ispra (IT)
Bibliographic Reference: Article: International Journal of Remote Sensing (1996)
Record Number: 199610878 / Last updated on: 1996-09-16
Category: PUBLICATION
Original language: en
Available languages: en
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