Morphological Attribute Profiles for the Analysis of Very High Resolution Images

被引:635
作者
Dalla Mura, Mauro [1 ,2 ]
Benediktsson, Jon Atli [2 ]
Waske, Bjoern [3 ]
Bruzzone, Lorenzo [4 ]
机构
[1] Univ Trento, Dept Informat Engn & Comp Sci, Remote Sensing Grp, I-38123 Trento, Italy
[2] Univ Iceland, Fac Elect & Comp Engn, IS-101 Reykjavik, Iceland
[3] Univ Bonn, Inst Geodesy & Geoinformat, Dept Photogrammetry, D-53115 Bonn, Germany
[4] Univ Trento, Dept Informat Engn & Comp Sci, I-38123 Povo, Italy
来源
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2010年 / 48卷 / 10期
关键词
Classification; mathematical morphology; morphological attribute profiles (APs); morphological profiles (MPs); object detection; remote sensing; very high resolution (VHR) images; REMOTE-SENSING IMAGES; HYPERSPECTRAL DATA; CLASSIFICATION; OPENINGS;
D O I
10.1109/TGRS.2010.2048116
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Morphological attribute profiles (APs) are defined as a generalization of the recently proposed morphological profiles (MPs). APs provide a multilevel characterization of an image created by the sequential application of morphological attribute filters that can be used to model different kinds of the structural information. According to the type of the attributes considered in the morphological attribute transformation, different parametric features can be modeled. The generation of APs, thanks to an efficient implementation, strongly reduces the computational load required for the computation of conventional MPs. Moreover, the characterization of the image with different attributes leads to a more complete description of the scene and to a more accurate modeling of the spatial information than with the use of conventional morphological filters based on a predefined structuring element. Here, the features extracted by the proposed operators were used for the classification of two very high resolution panchromatic images acquired by Quickbird on the city of Trento, Italy. The experimental analysis proved the usefulness of APs in modeling the spatial information present in the images. The classification maps obtained by considering different APs result in a better description of the scene (both in terms of thematic and geometric accuracy) than those obtained with an MP.
引用
收藏
页码:3747 / 3762
页数:16
相关论文
共 30 条
[1]   Automatic detection of geospatial objects using multiple hierarchical segmentations [J].
Akcay, H. Goekhan ;
Aksoy, Selim .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2008, 46 (07) :2097-2111
[2]   Improved Classification of VHR Images of Urban Areas Using Directional Morphological Profiles [J].
Bellens, Rik ;
Gautama, Sidharta ;
Martinez-Fonte, Leyden ;
Philips, Wilfried ;
Chan, Jonathan Cheung-Wai ;
Canters, Frank .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2008, 46 (10) :2803-2813
[3]   Classification of hyperspectral data from urban areas based on extended morphological profiles [J].
Benediktsson, JA ;
Palmason, JA ;
Sveinsson, JR .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2005, 43 (03) :480-491
[4]   Classification and feature extraction for remote sensing images from urban areas based on morphological transformations [J].
Benediktsson, JA ;
Pesaresi, M ;
Arnason, K .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2003, 41 (09) :1940-1949
[5]   Attribute openings, thinnings, and granulometries [J].
Breen, EJ ;
Jones, R .
COMPUTER VISION AND IMAGE UNDERSTANDING, 1996, 64 (03) :377-389
[6]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[7]  
BRUZZONE L, 2008, P IGARSS, V2, P265
[8]   Classification of remote sensing images from urban areas using a fuzzy possibilistic model [J].
Chanussot, J ;
Benediktsson, JA ;
Fauvel, M .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2006, 3 (01) :40-44
[9]   Exploiting SAR and VHR Optical Images to Quantify Damage Caused by the 2003 Bam Earthquake [J].
Chini, Marco ;
Pierdicca, Nazzareno ;
Emery, William J. .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2009, 47 (01) :145-152
[10]  
Congalton R.G., 2008, Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, DOI DOI 10.1201/9781420055139