Abstract
This paper presents a hierarchical classification approach for Synthetic Aperture Radar (SAR) images. The Conditional Random Field (CRF) and Bayesian Network (BN) are employed to incorporate prior knowledge into this approach for facilitating SAR image classification. (1) A multilayer region pyramid is constructed based on multiscale oversegmentation, and then, CRF is used to model the spatial relationships among those extracted regions within each layer of the region pyramid; the boundary prior knowledge is exploited and integrated into the CRF model as a strengthened constraint to improve classification performance near the boundaries. (2) Multilayer BN is applied to establish the causal connections between adjacent layers of the constructed region pyramid, where the classification probabilities of those sub-regions in the lower layer, conditioned on their parents’ regions in the upper layer, are used as adjacent links. More contextual information is taken into account in this framework, which is a benefit to the performance improvement. Several experiments are conducted on real ESAR and TerraSAR data, and the results show that the proposed method achieves better classification accuracy.
Highlights
IntroductionSynthetic Aperture Radar (SAR) provides two-dimensional images independent from weather, daylight and cloud coverage conditions and has various applications, such as mapping, urban planning, disaster prevention [1], etc
The combination of a priori knowledge and image data itself plays an important role in performing robust and effective Synthetic Aperture Radar (SAR) image classification. To exploit this potential contextual information to improve classification accuracy, this paper presents a hierarchical classification framework based on the multilayer Bayesian network [29] and conditional random field
This paper has presented a hierarchical classification method for SAR images
Summary
Synthetic Aperture Radar (SAR) provides two-dimensional images independent from weather, daylight and cloud coverage conditions and has various applications, such as mapping, urban planning, disaster prevention [1], etc. Among these applications, terrain classification is one of the extremely active research interests. An increasing number of papers specific to this topic have appeared over the last three decades; these proposed methods can be roughly cast into three categories: polarimetric target decomposition, feature extraction and model construction. The polarimetric target decomposition method has been widely used in SAR image classification; the general idea behind this method is to represent the average backscattering as the sum of independent components. Since the polarization information is usually partially polarized, Remote Sens. 2017, 9, 96; doi:10.3390/rs9010096 www.mdpi.com/journal/remotesensing
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