Feature-Screened and Structure-Constrained Deep Forest for Unsupervised SAR Image Change Detection

Deep forest-based models for synthetic aperture radar (SAR) image change detection are generally challenged by noise sensitivity and high feature redundancy, which significantly degrade the prediction performance. To address these issues, this article proposes a structure-constrained and feature-screened deep forest, abbreviated as SC-FS-DF, for SAR image change detection. In preclassification, a fuzzy multi-neighborhood information C-means (FMNICM) clustering is proposed to generate high-quality pseudo-labels. It introduces the edge information, the non-local and intra-superpixel neighborhoods into the objective function of fuzzy local information C-means, thus suppressing the speckle noise and constraining structures of targets. In sample learning and label prediction module, a feature-screened deep forest (FS-DF) framework is constructed by combining feature importance and redundancy analysis with a dropout strategy, thus screening out the non-informative features and meanwhile retaining the informative ones for learning at each cascade layer. Finally, a novel energy function fusing the non-local and superpixel information is derived for refining the detection map generated by FS-DF, further preserving fine details and edge locations. Extensive comparison and ablation experiments on five real SAR datasets verify the effectiveness and robustness of the proposed SC-FS-DF, and demonstrate that the SC-FS-DF can well screen the high-dimensional features in change detection and constrain the structures of targets.