Automatic matching of multimodal remote sensing images remains a vital yet challenging task, particularly for remote sensing and computer vision applications. Most traditional methods mainly focus on key point detection and description of the original image, thus ignoring the deep semantic feature information such as semantic road features, with the result that the traditional method can not effectively resist nonlinear grayscale distortion, and has low matching efficiency and poor accuracy. Motivated by this, this paper proposes a novel automatic matching method named LURF via learned unstructured road features for the multimodal images. There are four main contributions in LURF. To begin with, the semantic road features were extracted from multimodal images based on segmentation model CRESIv2. Next, based on semantic road features, a stable and reliable intersection point detector has been proposed to detect unstructured key points. Moreover, a local entropy descriptor has been designed to describe key points with the local skeleton feature. Finally, a global optimization strategy is adopted to achieve the correct matching. The extensive experimental results demonstrate that the proposed LURF outperforms other state-of-the-art methods in terms of both accuracy and efficiency on different multimodal image data sets.

Automatic image registration of multimodal urban remote sensing images remains a critical challenging task in remote sensing image analysis due to significant nonlinear radiation distortions between multimodal image pairs; most of the traditional methods focus on the feature point detection and its local description and ignore the robust road information in multimodal urban remote sensing images. Motivated by this, we propose a fast and robust registration method for multimodal urban remote sensing images via road intersection triangular features. The proposed method obtains three main stages: Road lines extraction from images, intersection triangular feature construction, and triangular feature matching. The qualitative and quantitative experimental results show that the proposed method significantly outperforms other state-of-the-art methods, even when others completely fail to achieve the registration task of cross-modal images, our method still maintains good robustness and matching efficiency.

Seeking reliable correspondence between multispectral images is a fundamental and important task in computer vision. To overcome the nonlinearity problem occurring in multispectral image matching, a novel, edge-feature-based maximum clique-matching frame (EMCM) is proposed, which contains three main parts: (1) a novel strong edge binary feature descriptor, (2) a new correspondence-ranking algorithm based on keypoint distinctiveness analysis algorithms in the feature space of the graph, and (3) a false match removal algorithm based on maximum clique searching in the correspondence space of the graph considering both position and angle consistency. Extensive experiments are conducted on two standard multispectral image datasets with respect to the three parts. The feature-matching experiments suggest that the proposed feature descriptor is of high descriptiveness, robustness, and efficiency. The correspondence-ranking experiments validate the superiority of our correspondences-ranking algorithm over the nearest neighbor algorithm, and the coarse registration experiments show the robustness of EMCM with varied interferences.