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.

中文翻译：

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基于互易变换的联合深度和广泛学习，用于异构图像的变化检测


随着遥感成像技术的快速发展，异构图像变化检测（CD）已成为业界的热门话题。鉴于异构图像独特的物理特性，直接提取变化信息很困难。一些将异构图像转换为共同特征域的模型可能是有益的。然而，变换可能会受到并非域差异的变化区域的影响，这进一步降低了 CD 的准确性。为了解决这个问题，我们提出了一种基于互易变换的联合深度和广泛学习（RTDBL）模型，用于具有异构图像的CD。在RTDBL模型中，为了快速提取特征，设计了深度特征提取（DFE）模块，无需训练。此外，为了直接突出变化信息并消除变化区域的影响，设计了互易异构节点变换（RHNT）模块来构造回归函数以实现互易变换。随后，为了实现跨空间信息交互，提出了结构节点提取（SNE）模块来获取结构节点。为了有效利用上述信息并探索异构节点的连接，开发了异构对偶广泛学习（HDBL）来预测变化图。据我们所知，这是将深度学习和广泛学习结合起来进行异构图像 CD 的首次尝试。通过对四个广泛使用的数据集进行实验分析，并与十个最先进的模型进行比较，证明了所提出的 RTDBL 的有效性。