+ Site Statistics
+ Search Articles
+ Subscribe to Site Feeds
EurekaMag Most Shared ContentMost Shared
EurekaMag PDF Full Text ContentPDF Full Text
+ PDF Full Text
Request PDF Full TextRequest PDF Full Text
+ Follow Us
Follow on FacebookFollow on Facebook
Follow on TwitterFollow on Twitter
Follow on LinkedInFollow on LinkedIn

+ Translate

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

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

Applied Optics 55(35): 10038-10044

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.

(PDF emailed within 1 workday: $29.90)

Accession: 058355500

Download citation: RISBibTeXText

PMID: 27958408

Related references

Robust Multi-Focus Image Fusion Using Multi-Task Sparse Representation and Spatial Context. IEEE Transactions on Image Processing 25(5): 2045-2058, 2016

Multi-focus image fusion using dictionary-based sparse representation. Information Fusion 25: 72-84, 2015

A general framework for image fusion based on multi-scale transform and sparse representation. Information Fusion 24: 147-164, 2015

Sparse representation for tumor classification based on feature extraction using latent low-rank representation. Biomed Research International 2014: 420856-420856, 2015

Sparse Representation Based Multi-Instance Learning for Breast Ultrasound Image Classification. Computational and Mathematical Methods in Medicine 2017: 7894705-7894705, 2017

Joint Sparse Representation and Robust Feature-Level Fusion for Multi-Cue Visual Tracking. IEEE Transactions on Image Processing 24(12): 5826-5841, 2015

Medical Image Fusion Based on Feature Extraction and Sparse Representation. International Journal of Biomedical Imaging 2017: 3020461-3020461, 2017

Self-supervised sparse coding scheme for image classification based on low rank representation. Plos One 13(6): E0199141-E0199141, 2018

Separable vocabulary and feature fusion for image retrieval based on sparse representation. Neurocomputing 236: 14-22, 2017

Sparse representation-based robust face recognition by graph regularized low-rank sparse representation recovery. Neurocomputing 164: 220-229, 2015

Identification Method of Gas-Liquid Two-phase Flow Regime Based on Image Multi-feature Fusion and Support Vector Machine. Chinese Journal of Chemical Engineering 16(6): 832-840, 2008

Hypergraph-based multi-example ranking with sparse representation for transductive learning image retrieval. Neurocomputing 101: 94-103, 2013

Multi-focal nematode image stack classification using a projection-based multi-linear method. Machine Vision and Applications 29(1): 135-144, 2018

Image classification based on low-rank matrix recovery and Naive Bayes collaborative representation. Neurocomputing 169: 110-118, 2015

Joint Low-Rank and Sparse Principal Feature Coding for Enhanced Robust Representation and Visual Classification. IEEE Transactions on Image Processing 25(6): 2429-2443, 2016