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Classification of Remote Sensing Images Using Attribute Profiles and Feature Profiles from Different Trees: A Comparative Study

Minh-Tan Pham 1 Erchan Aptoula 2 Sébastien Lefèvre 1
1 OBELIX - Environment observation with complex imagery
IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE, UBS - Université de Bretagne Sud
Abstract : The motivation of this paper is to conduct a comparative study on remote sensing image classification using the morphological attribute profiles (APs) and feature profiles (FPs) generated from different types of tree structures. Over the past few years, APs have been among the most effective methods to model the image's spatial and contextual information. Recently, a novel extension of APs called FPs has been proposed by replacing pixel gray-levels with some statistical and geometrical features when forming the output profiles. FPs have been proved to be more efficient than the standard APs when generated from component trees (max-tree and min-tree). In this work, we investigate their performance on the inclusion tree (tree of shapes) and partition trees (alpha tree and omega tree). Experimental results from both panchromatic and hyperspectral images again confirm the efficiency of FPs compared to APs.
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Submitted on : Wednesday, November 13, 2019 - 6:36:50 PM
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Minh-Tan Pham, Erchan Aptoula, Sébastien Lefèvre. Classification of Remote Sensing Images Using Attribute Profiles and Feature Profiles from Different Trees: A Comparative Study. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018, Valencia, Spain. ⟨10.1109/IGARSS.2018.8517735⟩. ⟨hal-01969020⟩

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