NDLI: Fusion of Global and Local Descriptors for Remote Sensing Image Classification

Content Provider	IEEE Xplore Digital Library
Author	Risojevic, V. Babic, Z.
Copyright Year	2004
Abstract	Very high resolution remote sensing images offer increased amount of details available for image interpretation. However, despite enhanced resolution, these details result in spectral inhomogeneities, making automated image classification more difficult. In this letter, we propose to combine texture and local image features to address this problem. We first address the enhanced Gabor texture descriptor which is a global descriptor based on cross correlations between subbands and show that it achieves very good results in classification of aerial images showing a single thematic class. Next, the performances obtained on individual land cover/land use classes using our global texture descriptor and local scale-invariant feature transform descriptor are compared. We identify classes of images best suited for each descriptor and argue that these descriptors encode complementary information. Finally, a hierarchical approach for the fusion of global and local descriptors is proposed and evaluated over a number of classifiers. The proposed descriptor fusion approach exhibits significantly improved classification results, reaching the accuracy of around 90%.
Sponsorship	IEEE Geoscience and Remote Sensing Society
Starting Page	836
Ending Page	840
Page Count	5
File Size	572620
File Format	PDF
ISSN	1545598X
Volume Number	10
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Remote sensing Accuracy Standards Correlation Kernel Support vector machines stacked generalization Gabor texture descriptor remote sensing image classification scale-invariant feature transform (SIFT) descriptor
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Geotechnical Engineering and Engineering Geology

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