Remote Sensing Image Retrieval With Global Morphological Texture Descriptors

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Date

2014

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Publisher

Ieee-inst Electrical Electronics Engineers inc

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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

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Citation

149

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Q1

Scopus Q

Q1

Source

Volume

52

Issue

5

Start Page

3023

End Page

3034