期刊
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
卷 12, 期 5, 页码 1131-1135出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2014.2385771
关键词
Effective baseline; feature extraction; fractional Fourier transform (FrFT); interferometric synthetic aperture radar (InSAR); phase gradient
类别
资金
- German Academic Exchange Service (DAAD)
In the literature, scene recognition from interferometric synthetic aperture radar (InSAR) images has been mainly focused on the joint use of the backscatter intensity and the coherence between interferometric image pairs. However, the terrain height information residing in the interferometric phase requires further exploration for classification purposes. In this letter, taking the interferometric phase information into account together with the backscatter intensity, the whole complex-valued InSAR image is exploited for feature extraction. In addition, a new complex-valued phase-gradient InSAR (PGInSAR) image is defined. A fractional-Fourier-transform-based feature extraction, which was proposed for the classification of single-look complex (SLC) SAR images, is adopted for InSAR and PGInSAR images. For patch-based classification, an image database is generated from bistatic pairs acquired from the same terrain with three different effective baselines. The supervised K-nearest neighbor classification results show that InSAR outperforms SLC by 15%, whereas PGInSAR introduces further 10% improvement over InSAR or a total improvement of 27% over SLC. Moreover, PGInSAR is found to be more robust to effective baseline changes than InSAR, which makes PGInSAR a better candidate for feature extraction.
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