PolSAR Image Classification Based on Robust Low-Rank Feature Extraction and Markov Random Field
This work addresses speckle noise and spatial consistency in PolSAR image classification for remote sensing applications, representing an incremental improvement.
The authors tackled PolSAR image classification by combining robust low-rank feature extraction to remove speckle noise and Markov random fields for spatial smoothness, achieving promising classification performance and spatial consistency on two benchmark datasets.
Polarimetric synthetic aperture radar (PolSAR) image classification has been investigated vigorously in various remote sensing applications. However, it is still a challenging task nowadays. One significant barrier lies in the speckle effect embedded in the PolSAR imaging process, which greatly degrades the quality of the images and further complicates the classification. To this end, we present a novel PolSAR image classification method, which removes speckle noise via low-rank (LR) feature extraction and enforces smoothness priors via Markov random field (MRF). Specifically, we employ the mixture of Gaussian-based robust LR matrix factorization to simultaneously extract discriminative features and remove complex noises. Then, a classification map is obtained by applying convolutional neural network with data augmentation on the extracted features, where local consistency is implicitly involved, and the insufficient label issue is alleviated. Finally, we refine the classification map by MRF to enforce contextual smoothness. We conduct experiments on two benchmark PolSAR datasets. Experimental results indicate that the proposed method achieves promising classification performance and preferable spatial consistency.