NDLI: Hierarchical Feature-Based Classification Approach for Fast and User-Interactive SAR Image Interpretation

Content Provider	IEEE Xplore Digital Library
Author	Pons Bernad, G. Denise, L. Refregier, P.
Copyright Year	2004
Abstract	The framework of this paper is focused on semiautomatic fast recognition of areas of interest for fast and user-interactive synthetic aperture radar (SAR) image interpretation for which only a unique intensity SAR image is available. The goal is to label regions into classes significant to a given application in an image, as rapidly as possible. A semiautomated ldquoroughrdquo classification is proposed. It defines the information extraction as a two-level procedure. The technique is based on a first partition image into homogeneous regions using the approach proposed by Galland Then, discrimination characteristics are determined in each homogeneous region. This allows one to automatically obtain a first segmentation of the image into semantic regions of interest. Finally, this segmentation can be easily modified by a user in a limited computational time. At this level, they are considered as ldquoobjects,rdquo to identify which typical class of ground it can be attached to. Among a large set of tested measures, we have selected the most pertinent ones for the considered SAR images. In fact, we will see that to obtain an accurate measures estimation, measures need to be estimated inside a neighborhood as homogeneously as possible. This can be achieved with a reasonable confidence in the proposed approach due to the homogeneity properties of the segmentation technique applied. In this paper, we focus on linear structures, urban structures, agricultural parcels, and forest areas extraction in SAR images.
Sponsorship	IEEE Geoscience and Remote Sensing Society
Starting Page	117
Ending Page	121
Page Count	5
File Size	593510
File Format	PDF
ISSN	1545598X
Volume Number	6
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Image analysis Partitioning algorithms Synthetic aperture radar Radar imaging Image recognition Data mining Testing Image classification radar image segmentation Radar image classification radar image region analysis
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Geotechnical Engineering and Engineering Geology

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