Complex Scene Classification of PolSAR Imagery based on a Self-paced Learning Approach
This work addresses a domain-specific challenge in remote sensing for PolSAR image analysis, offering an incremental improvement in classification accuracy for complex scenes.
The paper tackles the problem of classifying complex scenes in polarimetric synthetic aperture radar (PolSAR) imagery, where existing methods struggle due to noise and similar scattering properties across land cover types, and proposes a self-paced learning approach with neighborhood constraints, achieving good performance on three real PolSAR images.
Existing polarimetric synthetic aperture radar (PolSAR) image classification methods cannot achieve satisfactory performance on complex scenes characterized by several types of land cover with significant levels of noise or similar scattering properties across land cover types. Hence, we propose a supervised classification method aimed at constructing a classifier based on self-paced learning (SPL). SPL has been demonstrated to be effective at dealing with complex data while providing classifier. In this paper, a novel Support Vector Machine (SVM) algorithm based on SPL with neighborhood constraints (SVM_SPLNC) is proposed. The proposed method leverages the easiest samples first to obtain an initial parameter vector. Then, more complex samples are gradually incorporated to update the parameter vector iteratively. Moreover, neighborhood constraints are introduced during the training process to further improve performance. Experimental results on three real PolSAR images show that the proposed method performs well on complex scenes.