Invariant feature extraction is a critical challenge in intelligent image processing, particularly with the rapid advancement of multi-source/modal imaging. Cross-modal matching has attracted considerable attention, yet current studies primarily focus on targeted modalities rather than realizing a general approach. In this paper, cross-arbitrary-modal image invariant feature extraction and matching is studied. Inspired by human vision, a purely handcrafted invariant feature transform is proposed for universal cross-modal image matching, named Hierarchical Intrinsic Major Orientation (HIMO). Based on orientation information, a full-chain non-data-driven algorithm is designed that hinges on an Intrinsic Major Orientation (IMO) extraction. The HIMO incorporates a novel keypoint detector utilizing Difference-of-Feature Suppression (DoFS), a Polar-Pyramid descriptor (PolarP), and a Cascaded Dynamic Multi-scale Strategy (CDMS) to effectively address common challenges such as intensity distortion, rotation, scale differences, geometric deformation, and image noise. To validate the proposed method, two massive cross-modal datasets-General Cross-modal Zone (GCZ) and Wide-area Diverse Sources (WDS)-are introduced, alongside two practical evaluation metrics. Comprehensive experiments compared with 10 traditional and 15 deep-learning state-of-the-art algorithms on 5 datasets fully demonstrate that the proposed HIMO achieves superior performance in terms of robustness, stability, and generalization across diverse imaging conditions.

Cross-modal remote sensing image matching serves as a key technique for collaborative utilization of multi-source information. However, modal differences and geometric distortions between multi-source images pose challenges to existing methods in terms of robustness and generalization. To achieve feature interaction in cross-modal scenes, this paper proposes SwinMatcher, an end-to-end matching model based on the Transformer architecture. Innovatively proposing the window/shifted-window cross-attention based on the window/shifted-window self-attention mechanisms of Swin Transformer, SwinMatcher enables efficient cross-modal feature interaction and multi-scale contextual modeling. It also incorporates a learnable matching module to directly generate semi-dense correspondences. Moreover, a cross-modal remote sensing image matching dataset is generated, which encompasses four modalities: visible light, synthetic aperture radar (SAR), light detection and ranging (LiDAR), and map, distributed across four representative scenes. The dataset includes 400 samples produced via random homography transformations, designed to enhance modal diversity and scene complexity. Experiments demonstrate that SwinMatcher outperforms state-of-the-art methods on this new dataset as well as public benchmarks, exhibiting superior robustness under complex scenes involving coupled modal and geometric distortions. The proposed method and dataset provide novel solutions and evaluation benchmarks for cross-modal remote sensing image matching. The code and testing dataset will be made publicly available at https://github.com/LotrL/SwinMatcher.

To address the challenges posed by extensive modality differences in image matching tasks, this paper proposes a cascaded learning framework. It guides the optimization of an end-to-end matching model via a dynamic data engine, which can provide sufficient cross-modal training data to support the model’s full adaptation to cross-modal features. The data engine integrates a random homography transformation module and a lightweight image generation model, enabling the online synthesis of cross-modal image pairs with geometric variations and diverse styles. This provides the matching model with rich cross-modal stimulation. The matching model adopts a hybrid architecture combining a CNN backbone and Transformer attention mechanisms, which integrates multi-scale local feature extraction with global context modeling. By adopting the proposed stepwise aggregation strategy, the efficiency of feature extraction is well ensured. Subsequently, a coarse-to-fine matching strategy is employed to achieve high accuracy and robustness of feature alignment. Comprehensive experiments on both self-collected and public cross-modal image matching datasets demonstrate that the proposed DGIM outperforms existing state-of-the-art approaches in cross-modal matching performance while achieving a good balance between efficiency and effectiveness. It also exhibits broad practical potential across multiple fields and scenes. This work provides novel solutions and evaluation benchmarks for cross-modal image matching tasks. The code and testing dataset will be made publicly available at https://github.com/LotrL/DGIM.