Unsupervised Land Cover/Land Use Classification Using PolSAR Imagery Based on Scattering Similarity
- 教研院－已发表论文 
This paper presents a new unsupervised land cover/land use classification scheme using polarimetric synthetic aperture radar (PolSAR) imagery based on polarimetric scattering similarity. Compared with the H/alpha classification scheme based on a dominant "average" scattering mechanism, the proposed scheme has such advantages as the following: 1) The major scattering mechanism represents a target scattering in the low-entropy case; 2) it also represents both the major and minor scattering mechanisms in the medium-entropy case; and 3) all the scattering mechanisms in the high-entropy case can be represented. The major and minor scattering mechanisms have been identified automatically based on the relative magnitude of multiple-scattering similarities. The canonical scattering corresponding to maximum scattering similarity is regarded as the major scattering mechanism. The result obtained using the National Aeronautics and Space Administration/Jet Propulsion Laboratory's AIRSAR L-band PolSAR imagery reveals that the proposed scheme is more effective as compared to the existing models and promises to increase the accuracy of the classification and interpretation.