Complex Scene Classification of PolSAR Imagery based on a Self-paced Learning Approach

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.

💡 Research Summary

The paper addresses the challenging problem of classifying polarimetric synthetic aperture radar (PolSAR) images that contain complex scenes—multiple land‑cover types, high noise levels, and similar scattering mechanisms across classes. Conventional classifiers such as standard SVM, Random Forest, deep neural networks, and the Wishart classifier often fail to deliver satisfactory accuracy under these conditions because they treat all training samples equally and lack mechanisms to cope with noisy or ambiguous data.

To overcome these limitations, the authors propose a novel supervised classification framework that integrates Self‑Paced Learning (SPL) with a Support Vector Machine (SVM) and augments the SPL regularization with spatial neighborhood constraints. SPL mimics human learning by first presenting “easy” samples (those with low loss) and gradually incorporating more difficult ones as a pace parameter λ increases. Two SPL regularizers are discussed in the literature; the paper adopts the linear regularizer, which yields a continuous weight v_i ∈