# Complex Scene Classification of PolSAR Imagery based on a Self-paced Learning Approach Source: [Complex Scene Classification of PolSAR Imagery based on a Self-paced Learning Approach]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:29:43.441Z Authors: Wenshuai Chen, Shuiping Gou, Xinlin Wang, Licheng Jiao, Changzhe Jiao, Alina Zare Published: 2019-03-18T03:31:02.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()