Multichannel image classification based on adaptive attribute profiles

Morphological Attribute Profiles serve as powerful tools for extracting meaningful features from remote sensing data. The construction of Morphological Attribute Profiles relies on two primary parameters: the choice of attribute type and the definition of a numerical threshold sequence. However, selecting an appropriate threshold sequence can be a difficult task, as an inappropriate choice can lead to an uninformative feature space. In this paper, we propose a semi-automatic approach based on the theory of Maximally Stable Extremal Regions to address this challenge. Our approach takes an increasing attribute type and an initial sequence of thresholds as input and locally adjusts threshold values based on region stability within the image. Experimental results demonstrate that our method significantly increases classification accuracy through the refinement of threshold values.