Remote Sensing Image Retrieval With Global Morphological Texture Descriptors
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Date
2014
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Ieee-inst Electrical Electronics Engineers inc
Abstract
In this paper, we present the results of applying global morphological texture descriptors to the problem of content-based remote sensing image retrieval. Specifically, we explore the potential of recently developed multiscale texture descriptors, namely, the circular covariance histogram and the rotation-invariant point triplets. Moreover, we introduce a couple of new descriptors, exploiting the Fourier power spectrum of the quasi-flat-zone-based scale space of their input. The descriptors are evaluated with the UC Merced Land Use-Land Cover data set, which has been only recently made public. The proposed approach is shown to outperform the best known retrieval scores, despite its shorter feature vector length, thus asserting the practical interest of global content descriptors as well as of mathematical morphology in this context.
Description
Aptoula, Erchan/0000-0001-6168-2883
Keywords
Content-based image retrieval (CBIR), mathematical morphology (MM), remote sensing, texture description
Turkish CoHE Thesis Center URL
Citation
149
WoS Q
Q1
Scopus Q
Q1
Source
Volume
52
Issue
5
Start Page
3023
End Page
3034