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.
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:
Citation counts are sourced monthly from and citation databases.
Citations counts from theindexing service can be viewed at the linked Google Scholar™ search.
Full-text downloads displays 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.
|Item Type:||Journal Article|
|Keywords:||Accuracy , Feature extraction , Histograms , Image color analysis , Neurons , Pixel , Vegetation mapping|
|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 01:27|
|Last Modified:||27 Jun 2011 03:58|
Repository Staff Only: item control page