Multi-stage classification method oriented to aerial image based on low-rank recovery and multi-feature fusion sparse representation

Xu Ma, Yongmei Cheng, Shuai Hao

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Automatic classification of terrain surfaces from an aerial image is essential for an autonomous unmanned aerial vehicle (UAV) landing at an unprepared site by using vision. Diverse terrain surfaces may show similar spectral properties due to the illumination and noise that easily cause poor classification performance. To address this issue, a multi-stage classification algorithm based on low-rank recovery and multi-feature fusion sparse representation is proposed. First, color moments and Gabor texture feature are extracted from training data and stacked as column vectors of a dictionary. Then we perform low-rank matrix recovery for the dictionary by using augmented Lagrange multipliers and construct a multi-stage terrain classifier. Experimental results on an aerial map database that we prepared verify the classification accuracy and robustness of the proposed method.

Original languageEnglish
Pages (from-to)10038-10044
Number of pages7
JournalApplied Optics
Volume55
Issue number35
DOIs
StatePublished - 10 Dec 2016

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