When Deep Learning Meets Broad Learning: A Unified Framework for Change Detection with Synthetic Aperture Radar Images

Change detection (CD) with synthetic aperture radar (SAR) images remains pivotal for environmental monitoring and disaster management. Deep learning has powerful feature extraction capabilities for CD, but suffers from complex architectures and limited interpretability. While BLSs demonstrate advantages in structural simplicity and interpretability, their feature representation capacity remains constrained. In high-precision CD with SAR images, strong feature representation capability is required, along with an uncomplicated framework and high interpretability. Therefore, a novel paradigm named PC-BiBL is proposed which achieves seamless integration of deep learning and broad learning. On the one hand, it employs a hierarchical cross-convolutional encoding (HCCE) module that uses pseudo-random cross-convolution (PCConv) for hierarchical cross-feature representation, aggregating contextual information. PCConv is an untrained convolution layer, which can utilize specialized pseudo-random kernels to extract features from bitemporal SAR images. On the other hand, since back-propagation algorithms are not required, the features can be directly fed into the bifurcated broad learning (BiBL) module for node expansion and direct parameter computation. BiBL constructs dual-branch nodes and computes their difference nodes, explicitly fusing bitemporal features while highlighting change information—an advancement over traditional BLS. Experiments on five SAR datasets demonstrate the state-of-the-art performance of PC-BiBL, surpassing existing methods in accuracy and robustness. Quantitative metrics and visual analyses confirm its superiority in handling speckle noise and preserving boundary information.