Non-Parametric Classification of Remotely Sensed Multispectral Image Data by Means of Matrix Representation of Multidimensional Histograms | Zendy

Minoru Inamura | Zendy

Open Access

Non-Parametric Classification of Remotely Sensed Multispectral Image Data by Means of Matrix Representation of Multidimensional Histograms

Author(s) -

Minoru Inamura

Publication year - 1994

Publication title -

journal of robotics and mechatronics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.257

H-Index - 19

eISSN - 1883-8049

pISSN - 0915-3942

DOI - 10.20965/jrm.1994.p0042

Subject(s) - histogram , multispectral image , pattern recognition (psychology) , artificial intelligence , multispectral pattern recognition , parametric statistics , image histogram , computer science , histogram matching , mathematics , computer vision , remote sensing , image (mathematics) , image processing , geography , image texture , statistics

The computer framing of land use maps using remotely sensed multispectral image data is identical with pattern classification for spectral reflectance of objects on earth's surface. In particular, the classification by the maximum likelihood method is the most popular method because it theoretically gives the highest correct classification rate on the condition that the statistical distribution of the image data be normal. However, the histogram of real image data is not a normal distribution. Actual histograms show the proper distributions to classes. This fact means that a histogram gives a spatial property of the class statistically. This paper described a newly developed non-parametric method by means of the matrix representations of multidimensional histograms and subimages.

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