Extended profiles with morphological attribute filters for the analysis of hyperspectral data

被引：349
作者：
Dalla Mura, Mauro [1 ,2 ]
Benediktsson, Jon Atli [2 ]
Waske, Bjoern [3 ]
Bruzzone, Lorenzo [1 ]
机构：
[1] Univ Trento, Dept Informat Engn & Comp Sci, I-38123 Povo, 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
来源：
INTERNATIONAL JOURNAL OF REMOTE SENSING | 2010年 / 31卷 / 22期
关键词：
CONNECTED OPERATORS; CLASSIFICATION; IMAGE; RECOGNITION; EXTRACTION;
D O I：
10.1080/01431161.2010.512425
中图分类号：
TP7 [遥感技术];
学科分类号：
081102 ; 0816 ; 081602 ; 083002 ; 1404 ;
摘要：
Extended attribute profiles and extended multi-attribute profiles are presented for the analysis of hyperspectral high-resolution images. These extended profiles are based on morphological attribute filters and, through a multi-level analysis, are capable of extracting spatial features that can better model the spatial information, with respect to conventional extended morphological profiles. The features extracted by the proposed extended profiles were considered for a classification task. Two hyperspectral high-resolution datasets acquired for the city of Pavia, Italy, were considered in the analysis. The effectiveness of the introduced operators in modelling the spatial information was proved by the higher classification accuracies obtained with respect to those achieved by a conventional extended morphological profile.
引用
收藏
页码：5975 / 5991
页数：17
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