Remote sensing image segmentation using geodesic-kernel functions and multi-feature spaces

Xuemei Zhao

doi:10.1016/j.patcog.2020.107333

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Remote sensing image segmentation using geodesic-kernel functions and multi-feature spaces

2020-08-01

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DOI：10.1016/j.patcog.2020.107333

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Abstract

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Image representation is the key factor influencing the accuracy of remote sensing image segmentation. Traditional algorithms rely on the pixel-wise characteristics exhibited in the feature space. They introduce spatial information by establishing the connections between neighboring pixels in the neighborhood system. But the spectral-spatial features cannot be well expressed. In this paper, a Riemannian manifold space is introduced to express the contextual information by jointly mapping the spectral features of a pixel and its neighboring ones on to it. To benefit from the expression ability and geometric properties of the Riemannian manifold, a data submanifold and a parameter submanifold are established to depict the characteristics of the detected image and all possible segmentation results. On the parameter submanifold, only points representing objects of the current segmentation are active. Then distance between a point on the data submanifold and an active point on the parameter submanifold is measured by a geodesic-kernel function. Consequently, four geodesic-kernel function-based manifold projection criteria are proposed to explore the complementation between features expressed in different feature spaces. Experiments on synthetic and real remote sensing images demonstrated that the proposed geodesic-kernel function-based manifold projection algorithm outperforms traditional ones and features expressed in different feature spaces did contain some complementary information. (C) 2020 Elsevier Ltd. All rights reserved.

Keywords:

Remote sensing

Image segmentation

Riemannian manifold

Manifold projection

Kernel function

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