A new approach for the morphological segmentation of high-resolution satellite imagery

TLDR

Morphological leveling and spectrum concepts underpin multiscale segmentation, which is suited for complex satellite images but can be challenged by low contrast, low resolution, and border effects that blur object/background distinctions. The study proposes a new segmentation method that leverages the morphological characteristic of connected components. The method formalizes the morphological characteristic of connected components by computing the derivative of the morphological profile within a multiscale segmentation framework. The method achieves robust segmentation on satellite images even with low radiometric contrast and low spatial resolution, as demonstrated by the provided examples.

Abstract

A new segmentation method based on the morphological characteristic of connected components in images is proposed. Theoretical definitions of morphological leveling and morphological spectrum are used in the formal definition of a morphological characteristic. In multiscale segmentation, this characteristic is formalized through the derivative of the morphological profile. Multiscale segmentation is particularly well suited for complex image scenes such as aerial or fine resolution satellite images, where very thin, enveloped and/or nested regions must be retained. The proposed method performs well in the presence of both low radiometric contrast and relatively low spatial resolution. Those factors may produce a textural effect, a border effect, and ambiguity in the object/background distinction. Segmentation examples for satellite images are given.