Academic
Publications
Classification and Extraction of Spatial Features in Urban Areas Using High-Resolution Multispectral Imagery

Classification and Extraction of Spatial Features in Urban Areas Using High-Resolution Multispectral Imagery,10.1109/LGRS.2006.890540,IEEE Geoscience

Classification and Extraction of Spatial Features in Urban Areas Using High-Resolution Multispectral Imagery   (Citations: 30)
BibTex | RIS | RefWorks Download
Classification and extraction of spatial features are investigated in urban areas from high spatial resolution multispectral imagery. The proposed approach consists of three steps. First, as an extension of our previous work [pixel shape index (PSI)], a structural feature set (SFS) is proposed to extract the statistical features of the direction-lines histogram. Second, some methods of dimension reduction, including independent component analysis, decision boundary feature extraction, and the similarity-index feature selection, are implemented for the proposed SFS to reduce information redundancy. Third, four classifiers, the maximum-likelihood classifier, backpropagation neural network, probability neural network based on expectation-maximization training, and support vector machine, are compared to assess SFS and other spatial feature sets. We evaluate the proposed approach on two QuickBird datasets, and the results show that the new set of reduced spatial features has better performance than the existing length-width extraction algorithm and PSI
Cumulative Annual
View Publication
The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.
Sort by: