Multispectral image matching plays a very important role in remote sensing image processing and can be applied for registering the complementary information captured by different sensors. Due to the nonlinear intensity difference in multispectral images, many classic descriptors designed for images of the same spectrum are unable to work well. To cope with this problem, this paper proposes a new local feature descriptor termed histogram of oriented structure maps (HOSM) for multispectral image matching tasks. This proposed method consists of three steps. First, we propose a new method based on local contrast to construct the structure guidance images from the multispectral images by transferring the significant contours from source images to results, respectively. Second, we calculate oriented structure maps with guided image filtering. In details, we first construct edge maps by the progressive Sobel filters to extract the common structure characteristics from the multispectral images, and then we compute the oriented structure maps by performing the guided filtering on the edge maps with the structure guidance images constructed in the first step. Finally, we build the HOSM descriptor by calculating the histogram of oriented structure maps in a local region of each interest point and normalize the feature vector. The proposed HOSM descriptor was evaluated on three commonly used datasets and was compared with several state-of-the-art methods. The experimental results demonstrate that the HOSM descriptor can be robust to the nonlinear intensity difference in multispectral images and outperforms other methods.

In the intelligent transportation system, vehicle detection is one of the essential technologies in obstacle avoidance and navigation, however the existing vehicle detection methods cannot meet the actual needs. This paper presents a vehicle detection method combines the intensity and distance information of point cloud, which improves the segmentation performance of nearby objects. Specifically, the data of point cloud collected by lidar is preprocessed first. Then the processed point cloud is clustered by combining its coordinate and intensity information. Finally, the clustered suspected targets are fed to the random forest classifier. Our method can efficiently detect and classify targets in large-scale disordered 3D point cloud with high accuracy. In the real-scanned Livox Mid-40 Lidar dataset, our proposed method improves the detection accuracy by 31% compared with the traditional Euclidean clustering.

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.

Automatic matching of multimodal images remains a critical challenging task in many remote sensing and computer vision applications. Due to significant nonlinear radiation distortions (NRDs) between multimodal images, it is difficult for many traditional feature matching methods which are sensitive to NRD to achieve satisfactory matching performance. To cope with this problem, this letter proposes a novel feature matching method named nonlinear consistent feature transform (NCFT) that is robust to large NRD. There are three main contributions to NCFT. First, we propose a new consistent feature map instead of image intensity for feature point detection and description, the consistent feature map encodes the structure information and provides a rich and robust feature. Second, we propose a mean-residual maximum index map (MR-MIM) for feature description and the MR-MIM is constructed from the Log-Gabor convolution sequence on the consistent feature map. Finally, the structure descriptors are built according to the MR-MIM, and multimodal image matching is achieved by computing the correspondence. The extensive experimental results demonstrate that NCFT can effectively overcome the problem of NRD, NCFT outperforms other state-of-the-art methods and improves the matching accuracy and robustness on different multimodal image datasets.

Depth completion is an essential task for the dense scene reconstruction on light detection and ranging (LiDAR)-camera system. Learning-based method achieves precise depth completion results on specific data sets. However, for the general outdoor scenes with insufficient labeled data sets, an efficient nonlearning method is still required. In this letter, from the geometrical constraint between depth and normal, a novel nonlearning normal guided depth completion method is proposed. For the objects in the outdoor scene, local brightness normal (LBN) constraint is derived from the Lambertian model. It is used to recover dense normal from RGB image and sparse normal. After that, we present a pipeline for depth completion with the guidance of dense normal. Extensive experiments on the KITTI depth completion data set demonstrate that our method achieves smaller root mean squared error (RMSE) than current nonlearning methods.