Identification of Seed Cells in Multispectral Images for GrowCut Segmentation

TL;DR

This paper introduces an automated multispectral image segmentation method that uses homogenous zones as seed cells for GrowCut, improving object-oriented classification without manual thresholding.

Contribution

The novel approach automatically identifies seed cells based on spectral homogeneity, enhancing GrowCut segmentation for high-resolution satellite images.

Findings

Effective segmentation of diverse land covers achieved

Automated seed selection reduces manual effort

Segmentation results are suitable for object-oriented classification

Abstract

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. We propose a technique that automatically segments the multispectral image while facing these issues. We identify in the image homogenous zones according to their spectral signatures through the use of morphological filters. These homogenous zones are representatives of 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 pixel's spectral signature, 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 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.