Paper Info
Title
Texture Retrieval From Very High Resolution Remote Sensing Images Using Local Extrema-Based Descriptors
Abstract
This paper proposes a novel approach for texture-based image indexing and retrieval in the scope of very high resolution (VHR) optical imagery. Our motivation is to take into account local textural features and structures inside each image to measure its similarity to other images. These local features are extracted for a set of characteristic points from the image using the local extrema-based descriptors (LED) from which the radiometric, spatial and gradient features of the local extrema pixels (i. e. maximums and minimums) are integrated to characterize local textures. Due to the fact that VHR images usually involve a variety of local textures which may weakly verify the stationarity hypothesis, an approach based on characteristic points like extrema pixels becomes relevant and effective. We perform our experimentation using texture databases extracted from VHR Pleiades images within the application of vineyard cultivation and oyster farming study. Retrieval results yielded by the proposed strategy are very promising and competitive compared to reference methods.
Year
DOI
Venue
2016
10.1109/IGARSS.2016.7729472
2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
Keywords
Field
DocType
Texture retrieval, very high resolution images, feature extraction, local extrema-based descriptor (LED), riemannian distance
Computer vision,Pattern recognition,Computer science,Image texture,Remote sensing,Search engine indexing,Maxima and minima,Feature extraction,Artificial intelligence,Pixel,Image resolution
Conference
ISSN
Citations 
PageRank 
2153-6996
0
0.34
References 
Authors
6
5
Name
Order
Citations
PageRank
Minh-Tan Pham1263.26
Grégoire Mercier260552.49
Olivier Regniers3212.46
Lionel Bombrun415020.59
Michel, J.5464.42