Hierarchical segmentation of urban satellite imagery

- We extract the features of the urban area satellite images using Gabor wavelet.
- Gabor wavelet applied in eight different directions (phases) considering sparsity and no need to add any sparse coding techniques.
- Categorization and segmentation of satellite images has done hierarchically using Bayesian classifier.
- Relaxation labeling eliminates unclassified and misclassified parts that it improves the accuracy rate.
- There is a combination among contextual, structural, spectral, and statistical information toward classification.

This paper proposes a method to combine contextual, structural, and spectral information for classification. The method is an integrated method for automatically classifying urban-area objects in very high-resolution satellite imagery. The approach addresses three aspects. First, the Gabor wavelet is applied to the image along with morphological operations, with the sparsity of the outcome considered. A Bayesian classifier then categorizes the different classes, such as buildings, roads, open areas, and shadows. There are some false positives (wrong classification), and false negatives (non-classification) in the initial results. These results can be corrected by the relaxation labeling categorization of the unknown regions. The novelty of the proposed approach lies in the extensive use of spatiotemporal features considering the sparsity of urban objects. The results indicate improvement in classification through relaxation labeling compared with existing methods.

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