Identification of Seed Cells in Multispectral Images for GrowCut Segmentation

The segmentation of satellite images is a necessary step to perform object-oriented image classification, which has become relevant due to its applicability on images with a high spatial resolution. To perform object-oriented image classification, the studied image must first be segmented in uniform regions. This segmentation requires manual work by an expert user, who must exhaustively explore the image to establish thresholds that generate useful and representative segments without oversegmenting and without discarding representative segments. In this work, we propose a technique that automatically segments the multispectral image while facing these challenges. We start identifying in the image homogenous zone according to their spectral signatures through the use of morphological filters. These homogenous zones are representatives of the different types of land coverings in the image and are used as seeds for the GrowCut multispectral segmentation algorithm. GrowCut is a cellular automaton with competitive region growth, its cells are linked to every pixel in the image through three parameters: the spectral signature of the pixel, a label, and a strength factor that represents the strength with which a cell defends its label. The seed cells possess maximum strength and maintain their state throughout the automaton's evolution. Starting from seed cells, each cell in the image is iteratively attacked by its neighboring cells. When the defending cell has lower strength than the strength of attack, it takes the label of the attacking cell and updates its strength as equal to the strength of the defeating attack. The attack strength function returns values that are inversely proportional to the distance between pixels, meaning that pixels with similar spectral signatures are likely to take the same label. When the automaton stops updating its states, we obtain a segmented image where each pixel has taken the label of one of its cells. In this paper the algorithm was applied in an image acquired by Landsat8 on agricultural land of Calabozo, Guarico, Venezuela where there are different types of land coverings: agriculture, urban regions, water bodies, and savannas with different degrees of human intervention. The segmentation obtained is presented as irregular polygons enclosing geographical objects.