NDLI: Texture based image retrieval and classification of very high resolution maritime pine forest images

Content Provider	IEEE Xplore Digital Library
Author	Regniers, O. Da Costa, J.-P. Grenier, G. Germain, C. Bombrun, L.
Copyright Year	2013
Description	Author affiliation: IMS Lab., Univ. de Bordeaux, Talence, France (Regniers, O.; Da Costa, J.-P.; Grenier, G.; Germain, C.; Bombrun, L.)
Abstract	Textural analysis can bring valuable information in the classification or the segmentation process of land covers displaying regular patterns in very high resolution remotely sensed images. In this study, we investigate how features extracted by multivariate modeling of the local spatial dependence in the wavelet domain can efficiently capture the textural content of maritime pine forest images in comparison with a commonly used texture analysis approach, the GLCM. To evaluate the performances of the tested methods, we used a content based image retrieval framework and created a database of image patches representing different development stages of the forest stands. Results show that multivariate models display higher retrieval rates than GLCM-based methods with yet a higher sensitivity to the dominant orientation in anisotropic textures. These observations open up new perspectives in the use of multivariate modeling for textural features extraction in very high resolution image classification.
Sponsorship	IEEE Geosci. Remote Sens. Soc.
Starting Page	4038
Ending Page	4041
File Size	466306
Page Count	4
File Format	PDF
ISBN	9781479911141
ISSN	21536996
DOI	10.1109/IGARSS.2013.6723719
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-21
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Remote sensing Image retrieval Image resolution Object oriented modeling Vectors Feature extraction forest textural feature multivariate models GLCM very high resolution
Content Type	Text
Resource Type	Article

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