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Proceedings Article

A comparison of autonomous techniques for multispectral image analysis and classification

[+] Author Affiliations
Juan C. Valdiviezo-N., Carina Toxqui-Quitl, Alfonso Padilla-Vivanco

Univ. Politécnica de Tulancingo (Mexico)

Gonzalo Urcid

Instituto Nacional de Astrofísica, Óptica y Electrónica (Mexico)

Proc. SPIE 8499, Applications of Digital Image Processing XXXV, 849920 (October 15, 2012); doi:10.1117/12.930289
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From Conference Volume 8499

  • Applications of Digital Image Processing XXXV
  • Andrew G. Tescher
  • San Diego, California, USA | August 12, 2012

abstract

Multispectral imaging has given place to important applications related to classification and identification of objects from a scene. Because of multispectral instruments can be used to estimate the reflectance of materials in the scene, these techniques constitute fundamental tools for materials analysis and quality control. During the last years, a variety of algorithms has been developed to work with multispectral data, whose main purpose has been to perform the correct classification of the objects in the scene. The present study introduces a brief review of some classical as well as a novel technique that have been used for such purposes. The use of principal component analysis and K-means clustering techniques as important classification algorithms is here discussed. Moreover, a recent method based on the min-W and max-M lattice auto-associative memories, that was proposed for endmember determination in hyperspectral imagery, is introduced as a classification method. Besides a discussion of their mathematical foundation, we emphasize their main characteristics and the results achieved for two exemplar images conformed by objects similar in appearance, but spectrally different. The classification results state that the first components computed from principal component analysis can be used to highlight areas with different spectral characteristics. In addition, the use of lattice auto-associative memories provides good results for materials classification even in the cases where some spectral similarities appears in their spectral responses. © (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation

Juan C. Valdiviezo-N. ; Gonzalo Urcid ; Carina Toxqui-Quitl and Alfonso Padilla-Vivanco
" A comparison of autonomous techniques for multispectral image analysis and classification ", Proc. SPIE 8499, Applications of Digital Image Processing XXXV, 849920 (October 15, 2012); doi:10.1117/12.930289; http://dx.doi.org/10.1117/12.930289


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