Abstract
In this paper, a new multicriterion segmentation method has been proposed to be applied to satellite image of very high spatial resolution (VHSR). It is consisted of the following process: For each region of the grayscale image, a center of gravity has been calculated and it has been also selected a threshold for its histogram. According to a certain criteria, this approach has been based on the separation of the different classes of grayscale in an optimal way. The proposed approach has been tested on synthetic images, and then has applied to an urban environment for the classification of data in Quickbird images. The selected zone of study has been laid in Skhirate-Témara province, northwest of Morocco. Which is based on the Levine and Nazif criterion, this segmentation technique has given promising results compared those obtained using OTSU and K-means methods.
Highlights
Segmentation is the technique and procedure used to divide the image into different non-overlapping regions according to their characteristics
The formula of total inter-region disparity was defined as follows: where wRk is a weight associated to Rk that can be dependent of its area, for example, g k is the average of the gray-level of Rk. g I ðRkÞ can be generalized to a feature vector computed on the pixel values of the region Rk such as for LEV1. pRk Rj Corresponds to the length of the perimeter of the region Rk common to the perimeter of the region Rj
7 Conclusions In this work, we proposed a new multicriterion segmentation method based on the separation of different classes of gray levels in an optimal way according to certain criteria and applied it to very high spatial resolution (VHSR) satellite images
Summary
Segmentation is the technique and procedure used to divide the image into different non-overlapping regions according to their characteristics. Variant region of interest are classified to be homogenous, this is due to two main critical issues in color image segmentation: (1) what's the In this case, a number of classification algorithms, based on 2D histogram analysis, are obtained by multidimensional histogram projection which are focused on two color procedures. This work proposes a method that focuses on the separation of different classes of grayscale in an optimal way according to some criterion, using typical techniques of image segmentation. The proposed approach in [12] is justified by the simple reason that, in almost all cases, the segmentation process, based on the optimization of one criterion only, does not work very well for many images. Functions (criteria) that we chose are the modified within-class variance, the overall probability of error and the entropy. Pal and Pal proposed a new definition of entropy based on exponential gain information: HT ðtÞ
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