Soft thematic mapping from remotely sensed data: production, interpretation, and accuracy assessment

Elisabetta Binaghi

doi:10.1117/12.413911

19 January 2001 Soft thematic mapping from remotely sensed data: production, interpretation, and accuracy assessment

Elisabetta Binaghi

Proceedings Volume 4170, Image and Signal Processing for Remote Sensing VI; (2001) https://doi.org/10.1117/12.413911
Event: Europto Remote Sensing, 2000, Barcelona, Spain

Abstract

In order to move towards a more adequate classification methodology one issue that has received particular attention within the remote sensing community is the development of soft classification models in alternative to conventional hard classification techniques. In soft classification, pattern indeterminacy must be connected with different forms of uncertainty such as vagueness, ambiguity, resulting in gradual strength of membership to classes. The work is focused on the use of soft classification techniques for production of soft maps in which grades of membership to classes are the final, meaningful outputs. When soft land cover maps are generated, grades of membership are correlated to the percentages of coverage; when maps specifying more abstract themes are generated grades have to represent the human natural approximation with which patterns matches with cognitive categories. Despite the availability of several soft classification techniques, soft thematic mapping has not being very often employed and the majority of classifications are still based on hard paradigms and maps are presented in discrete form. Significant problems in the use of these techniques limit their diffusion. The aim of this paper is to analyze the above limitations in an attempt of contributing to their overcoming.

Citation Download Citation

Elisabetta Binaghi "Soft thematic mapping from remotely sensed data: production, interpretation, and accuracy assessment", Proc. SPIE 4170, Image and Signal Processing for Remote Sensing VI, (19 January 2001); https://doi.org/10.1117/12.413911

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KEYWORDS

Fuzzy logic

Remote sensing

Image classification

Neural networks

Neurons

Matrices

Associative arrays

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