Abstract
Abstract. Image segmentation is the foundation of further object-oriented image analysis, understanding and recognition. It is one of the key technologies in high resolution remote sensing applications. In this paper, a new fast image segmentation algorithm for high resolution remote sensing imagery is proposed, which is based on graph theory and fractal net evolution approach (FNEA). Firstly, an image is modelled as a weighted undirected graph, where nodes correspond to pixels, and edges connect adjacent pixels. An initial object layer can be obtained efficiently from graph-based segmentation, which runs in time nearly linear in the number of image pixels. Then FNEA starts with the initial object layer and a pairwise merge of its neighbour object with the aim to minimize the resulting summed heterogeneity. Furthermore, according to the character of different features in high resolution remote sensing image, three different merging criterions for image objects based on spectral and spatial information are adopted. Finally, compared with the commercial remote sensing software eCognition, the experimental results demonstrate that the efficiency of the algorithm has significantly improved, and the result can maintain good feature boundaries.
Highlights
With the development of remote sensing technology and the improvement of satellite spatial resolution, high resolution remote sensing images are widely used in various fields
Object-oriented image analysis starts with the crucial initial step of grouping neighbouring pixels into meaningful areas, which can be handled by image segmentation
fractal net evolution approach (FNEA) starts with the initial object layer and carries out the object merging process to get the final segmentation result
Summary
With the development of remote sensing technology and the improvement of satellite spatial resolution, high resolution remote sensing images are widely used in various fields. High resolution remote sensing images have clear details and rich spatial and texture information. In order to take full advantage of this information, we started paying attention to the objectoriented image analysis. Object-oriented image analysis starts with the crucial initial step of grouping neighbouring pixels into meaningful areas, which can be handled by image segmentation. Image segmentation is defined as a process of splitting an image into regions based on some criteria (intensity, colour, texture, orientation energy). The goal of image segmentation is to simplify or change the representation of an image into something that is more meaningful and easier to analysis. High resolution remote sening image segmentation, which is different from traditional image segmentation, contains many objects of different size, but most segmentation algorithm specify a spatial sclae at the object. We should be able to describe objects in a hierarchical scale, as a result we apply the mutil-scale image segmentaiton
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