Reciprocal Transformation-Based Joint Deep and Broad Learning for Change Detection With Heterogeneous Images

With the rapid development of remote sensing imaging technology, change detection (CD) with heterogeneous images has become a hot topic in the community. Given the distinct physical properties of heterogeneous images, it is difficult for direct extraction of change information. Some models that transform heterogeneous images into a mutual feature domain can be beneficial. However, the transformation may be influenced by the changed areas that are not the discrepancy of the domains, which further decreases the accuracy of CD. To solve the problem, we propose a reciprocal transformation-based joint deep and broad learning (RTDBL) model for CD with heterogeneous images. In the RTDBL model, in order to rapidly extract features, a deep feature extraction (DFE) module is designed without the need for training. In addition, for directly highlighting change information and eliminating the influence of changed areas, a reciprocal heterogeneous nodes transformation (RHNT) module is designed to construct regression functions for achieving reciprocal transformation. Subsequently, to achieve cross-spatial information interaction, a structural nodes extraction (SNE) module is proposed for obtaining structural nodes. For effectively utilizing aforementioned information and exploring the connections of heterogeneous nodes, a heterogeneous dual broad learning (HDBL) is developed to predict the change map. According to the best of our knowledge, this is the first attempt that joints deep learning and broad learning for CD with heterogeneous images. The efficacy of the proposed RTDBL is demonstrated through experimental analysis on four widely used datasets, in comparison with ten state-of-the-art models.