Derivation of land cover information by fuzzy clustering of remotely sensed imagery

Helmut Beissmann; Gernot Tutsch

doi:10.1117/12.301371

1 February 1998 Derivation of land cover information by fuzzy clustering of remotely sensed imagery

Helmut Beissmann, Gernot Tutsch

Proceedings Volume 3346, Sixth International Workshop on Digital Image Processing and Computer Graphics: Applications in Humanities and Natural Sciences; (1998) https://doi.org/10.1117/12.301371
Event: Sixth International Workshop on Digital Image Processing and Computer Graphics, 1997, Vienna, Austria

Abstract

Automatic pattern recognition by means of fuzzy logic had been applied to several fields during the last years. The spectral properties of different land cover types as seen in multiband images can also be interpreted as patterns in the dimension of gray values. Fuzzy clustering therefore is a new promising approach to mapping land cover from remotely send images. The traditional method of classifying a remotely sensed image is the transformation via a classification algorithm into a single classified image of the land surface, but natural landscapes present a continuum of variety at many different scales and a high proportion of the discretely sampled pixels within an image contains mixed spectral signature and are not easily placed into fixed thematic classes. The estimation of fuzzy memberships to vague classes of land cover more faithfully represents the true situation.

Citation Download Citation

Helmut Beissmann and Gernot Tutsch "Derivation of land cover information by fuzzy clustering of remotely sensed imagery", Proc. SPIE 3346, Sixth International Workshop on Digital Image Processing and Computer Graphics: Applications in Humanities and Natural Sciences, (1 February 1998); https://doi.org/10.1117/12.301371

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