Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification
- Resource Type
- Conference
- Authors
- Li, Zhengrong; Liu, Yuee; Hayward, Ross; Walker, Rodney
- Source
- 2010 IEEE International Conference on Image Processing Image Processing (ICIP), 2010 17th IEEE International Conference on. :2701-2704 Sep, 2010
- Subject
- Signal Processing and Analysis
Computing and Processing
Kernel
Feature extraction
Principal component analysis
Image color analysis
Vegetation mapping
Accuracy
Histograms
geographic object-based image analysis (GEOBIA)
color-texture feature fusion
kernel principal component analysis
local binary patters
vegetation classification
- Language
- ISSN
- 1522-4880
2381-8549
A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is employed to find nonlinear relationships of the extracted color and texture features. The maximum likelihood approach is used to estimate the intrinsic dimensionality, which is then used as a criterion for automatic selection of optimal feature set from the fused feature. The proposed method is evaluated using SVM as the benchmark classifier and is applied to object-based vegetation species classification using high spatial resolution aerial imagery. Experimental results demonstrate that great improvement can be achieved by using proposed feature fusion method.