Classification and Extraction of Spatial Features in Urban Areas Using High-Resolution Multispectral Imagery
- 23 April 2007
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Geoscience and Remote Sensing Letters
- Vol. 4 (2), 260-264
- https://doi.org/10.1109/lgrs.2006.890540
Abstract
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 PSIKeywords
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