Recent Developments from Attribute Profiles for Remote Sensing Image Classification
This work addresses the need for better spatial and contextual modeling in remote sensing image classification, but it is incremental as it builds on existing AP methods.
The paper revisits and discusses recent developments and extensions of morphological attribute profiles (APs) for remote sensing image classification, showing significant improvements over the original APs in comparative experiments on two datasets.
Morphological attribute profiles (APs) are among the most effective methods to model the spatial and contextual information for the analysis of remote sensing images, especially for classification task. Since their first introduction to this field in early 2010's, many research studies have been contributed not only to exploit and adapt their use to different applications, but also to extend and improve their performance for better dealing with more complex data. In this paper, we revisit and discuss different developments and extensions from APs which have drawn significant attention from researchers in the past few years. These studies are analyzed and gathered based on the concept of multi-stage AP construction. In our experiments, a comparative study on classification results of two remote sensing data is provided in order to show their significant improvements compared to the originally proposed APs.