Abstract
The new generation of artificial satellites is providing a huge amount of Earth observation images whose exploitation can report invaluable benefits, both economical and environmental. However, only a small fraction of this data volume has been analyzed, mainly due to the large human resources needed for that task. In this sense, the development of unsupervised methodologies for the analysis of these images is a priority. In this work, a new unsupervised segmentation algorithm for satellite images is proposed. This algorithm is based on the rough-set theory, and it is inspired by a previous segmentation algorithm defined in the RGB color domain. The main contributions of the new algorithm are: (i) extending the original algorithm to four spectral bands; (ii) the concept of the superpixel is used in order to define the neighborhood similarity of a pixel adapted to the local characteristics of each image; (iii) and two new region merged strategies are proposed and evaluated in order to establish the final number of regions in the segmented image. The experimental results show that the proposed approach improves the results provided by the original method when both are applied to satellite images with different spectral and spatial resolutions.
Highlights
Image segmentation is a basic, important step in high level image understanding
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
We propose the use of superpixels to define the neighborhood for the histon creation and the superpixels’ local average standard deviation to provide the expanse for its color sphere
Summary
Image segmentation is a basic, important step in high level image understanding. It serves as a bridge for the semantic gap between low level image processing and high level image analysis. 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.
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