QUT ePrints

A biologically inspired object spectral-texture descriptor and its application to vegetation classification in power-line corridors

Li, Zhengrong, Hayward, Ross F., Walker, Rodney A., & Liu, Yuee (2011) A biologically inspired object spectral-texture descriptor and its application to vegetation classification in power-line corridors. IEEE Geoscience and Remote Sensing Letters, 8(4), pp. 631-635.

View at publisher

Abstract

The use of appropriate features to represent an output class or object is critical for all classification problems. In this paper, we propose a biologically inspired object descriptor to represent the spectral-texture patterns of image-objects. The proposed feature descriptor is generated from the pulse spectral frequencies (PSF) of a pulse coupled neural network (PCNN), which is invariant to rotation, translation and small scale changes. The proposed method is first evaluated in a rotation and scale invariant texture classification using USC-SIPI texture database. It is further evaluated in an application of vegetation species classification in power line corridor monitoring using airborne multi-spectral aerial imagery. The results from the two experiments demonstrate that the PSF feature is effective to represent spectral-texture patterns of objects and it shows better results than classic color histogram and texture features.

Impact and interest:

2 citations in Scopus
Search Google Scholar™
2 citations in Web of Science®

Citation countsare sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

197 since deposited on 15 Apr 2011
52 in the past twelve months

Full-text downloadsdisplays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 41278
Item Type: Journal Article
Keywords: Accuracy , Feature extraction , Histograms , Image color analysis , Neurons , Pixel , Vegetation mapping
DOI: 10.1109/LGRS.2010.2098391
ISSN: 1545-598X
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100)
Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > GEOMATIC ENGINEERING (090900) > Photogrammetry and Remote Sensing (090905)
Divisions: Current > Research Centres > Australian Research Centre for Aerospace Automation
Past > Schools > Computer Science
Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > Schools > School of Engineering Systems
Copyright Owner: Copyright 2011 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 15 Apr 2011 11:27
Last Modified: 27 Jun 2011 13:58

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page