Skip to Main content Skip to Navigation
Journal articles

Feature Profiles from Attribute Filtering for Classification of Remote Sensing Images

Minh-Tan Pham 1 Erchan Aptoula 2 Sébastien Lefèvre 1
1 OBELIX - Environment observation with complex imagery
UBS - Université de Bretagne Sud, IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : This paper proposes a novel extension of morphological attribute profiles (APs) for classification of remote sensing data. In standard AP-based approaches, an input image is characterized by a set of filtered images achieved from the sequential application of attribute filters based on the image tree representation. Hence, only pixel values (i.e. gray levels) are employed to form the output profiles. In this paper, during the attribute filtering process, instead of outputting the gray levels, we propose to extract both statistical and geometrical features from the connected components (w.r.t tree nodes) to build the so-called feature profiles (FPs). These features are expected to better characterize the object or region encoded by each connected component. They are then exploited to classify remote sensing images. To evaluate the effectiveness of the proposed approach, supervised classification using the random forest classifier is conducted on the panchromatic Reykjavik image as well as the hyperspectral Pavia University data. Experimental results show the FPs provide a competitive performance compared against standard APs and thus constitute a promising alternative.
Document type :
Journal articles
Complete list of metadatas

Cited literature [31 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01672852
Contributor : Sébastien Lefèvre <>
Submitted on : Wednesday, November 13, 2019 - 6:43:29 PM
Last modification on : Friday, July 10, 2020 - 4:01:04 PM

File

jstars2018fp.pdf
Files produced by the author(s)

Identifiers

Citation

Minh-Tan Pham, Erchan Aptoula, Sébastien Lefèvre. Feature Profiles from Attribute Filtering for Classification of Remote Sensing Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, IEEE, 2018, 11 (1), pp.249-256. ⟨10.1109/JSTARS.2017.2773367⟩. ⟨hal-01672852⟩

Share

Metrics

Record views

805

Files downloads

275