Color image segmentation: Rough-set theoretic approach

Superpixel-Based Roughness Measure for Multispectral Satellite Image Segmentation

“…Differences between these lower and upper approximations in an intensity segment can be used to show the lack or presence of color homogeneity or spatial similarity. To measure these differences, in [19], a roughness index is proposed, the corresponding expression defined as:…”

“…Although the original roughness index-based segmentation performs better than the methods based on traditional histograms and histons, as can be seen in [19], this method uses to calculate the histons a color sphere based on a fixed neighborhood (specifically, the 24 neighbors surrounding each pixel) and a fixed color distance. This small neighborhood, unrelated to the specific geometry of the image, tends to emphasize compact regions.…”

“…In this work, the authors also define a way to combine spectral and spatial information, as a method to visualize and analyze color information in similar image regions. Segmentations based only on histons usually under-represent small homogeneous regions in the image, so Mushrif and Ray developed a solution using histons from the perspective of rough-set theory in [19]. This method shows better performance for image segmentation than both histogram-based methods and histon-based methods.…”

“…The simplicity behind both the concept approach presented in [19] and its implementation makes it a good choice for its use as an unsupervised segmentation method of multi-spectral satellite images. However, this proposal relies on a very limited definition of what can be considered as a neighborhood for a pixel that can yield an inaccurate definition of the color distribution, a problem exacerbated when the method is applied to multi-spectral images.…”

“…However, this proposal relies on a very limited definition of what can be considered as a neighborhood for a pixel that can yield an inaccurate definition of the color distribution, a problem exacerbated when the method is applied to multi-spectral images. This paper proposes both the extension of the works of Mushif and Ray in color image segmentation in [19], as an unsupervised multi-spectral satellite image segmentation method, and the application of a prior segmentation using superpixels, as a natural way to provide a neighborhood adapted to the characteristics of the image to improve the original proposal. Furthermore, as part of the extension of the original method to the multi-spectral domain, we also propose two different region-merging methods (one pixel related, one region related) in order to improve the performance of the commonly-used color distance-merging method and to maintain control over the total number of segments produced.…”