Abstract
Extended Attribute Profiles (EAPs), which are obtained by applying morphological attribute filters to an image in a multilevel architecture, can be used for the characterization of the spatial characteristics of objects in a scene. EAPs have proved to be discriminant features when considered for thematic classification in remote sensing applications especially when dealing with very high resolution images. Altimeter data (such as LiDAR) can provide important information, which being complementary to the spectral one can be valuable for a better characterization of the surveyed scene. In this paper, we propose a technique performing a classification of the features extracted with EAPs computed on both optical and LiDAR images, leading to a fusion of the spectral, spatial and elevation data. The experiments were carried out on LiDAR data along either with a hyperspectral and a multispectral image acquired on a rural and urban area of the city of Trento (Italy), respectively. The classification accuracies obtained pointed out the effectiveness of the features extracted by EAPs on both optical and LiDAR data for classification.
| Original language | English |
|---|---|
| Article number | 6237479 |
| Pages (from-to) | 856-865 |
| Number of pages | 10 |
| Journal | IEEE Journal on Selected Topics in Signal Processing |
| Volume | 6 |
| Issue number | 7 |
| DOIs | |
| Publication status | Published - 2012 |
Bibliographical note
Funding Information:Manuscript received February 09, 2012; revised May 15, 2012; accepted July 03, 2012. Date of publication July 11, 2012; date of current version October 12, 2012. This work was supported in part by the Icelandic Research Fund and the Research Fund of the University of Iceland. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Daya Sagar Behara.
Other keywords
- Attribute filters
- classification
- extended attribute profiles
- hyperspectral images
- LiDAR
- multispectral images