Fusing Local And Global Features For High-Resolution Scene Classification

Keywords

Codebookless model (CLM); Feature representation; Image descriptors; Rotation invariance; Saliency detection; Scene classification

Abstract

In this paper, a fused global saliency-based multiscale multiresolution multistructure local binary pattern (salM3LBP) feature and local codebookless model (CLM) feature is proposed for high-resolution image scene classification. First, two different but complementary types of descriptors (pixel intensities and differences) are developed to extract global features, characterizing the dominant spatial features in multiple scale, multiple resolution, and multiple structure manner. The micro/macrostructure information and rotation invariance are guaranteedin the global feature extraction process. For dense local feature extraction, CLM is utilized to model local enrichment scale invariant feature transform descriptor and dimension reductionisconducted via joint low-rank learning with support vector machine. Finally, a fused feature representation between salM3LBP and CLM as the scene descriptor to train a kernel-based extreme learning machine for scene classification is presented. The proposed approach is extensively evaluated on three challenging benchmark scene datasets (the 21-class landuse scene, 19-class satellite scene, and a newly available 30-class aerial scene), and the experimental results show that the proposed approach leads to superior classification performance compared with the state-of-the-art classification methods.

Publication Date

6-1-2017

Publication Title

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volume

10

Issue

6

Number of Pages

2889-2901

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/JSTARS.2017.2683799

Socpus ID

85018971855 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85018971855

This document is currently not available here.

Share

COinS