Recent Developments from Attribute Profiles for Remote Sensing Image Classification

Abstract : 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.
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Conference papers
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Contributor : Sébastien Lefèvre <>
Submitted on : Thursday, January 3, 2019 - 3:49:30 PM
Last modification on : Friday, January 11, 2019 - 4:23:19 PM

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  • HAL Id : hal-01969037, version 1
  • ARXIV : 1803.10036


Minh-Tan Pham, Sébastien Lefèvre, Erchan Aptoula, Lorenzo Bruzzone. Recent Developments from Attribute Profiles for Remote Sensing Image Classification. International Conference on Pattern Recognition and Artificial Intelligence, 2018, Montreal, Canada. ⟨hal-01969037⟩



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