The partitioning of an image may be defined as the division of its image plane into image objects. This paper presents a new methodology for partitioning multispectral images. It combines the analysis of the spectral properties of the pixels with the analysis
of their spatial properties. Spectral properties are studied in the multivariate histogram of the image, while spatial properties are analyzed in the multispectral gradient of the image. The histogram of the image is first segmented by a nonparametric algorithm. The segmented histogram allows the classification of all image pixels. Each resulting class is then separately filtered in order to remove all classified pixels having a high probability of being misclassified when considering spatial criteria. The filtered classes are used as seeds for boundary detection on the gradient of the original image. The class of the resulting regions is given by the class of the seeds that created those regions.