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Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the problem of image registration difficulties caused by the significant differences of multi-source remote sensing images due to the sensor type, temporal phase and illumination conditions, this paper proposes a multisource image registration method guided by first-order Gaussian steerable filters. Methods: First, based on the geometric reference information of the image, the overlapping area of the reference image and the image to be aligned in the image space is calculated, the overlapping area is uniformly partitioned with the reference image surface as the reference, the corresponding image block to be aligned is calculated by the rational function model and the digital elevation model, and the affine transformation model is established to geometrically correct the image block to be aligned, so as to realize the coarse registration between the local images. Second, for feature detection, anti-cluster features from accelerated segmentation test improved by the chunking uniformity checking strategy are constructed to obtain a large number of uniformly distributed feature points. And for feature description, a set of multi-scale, multi-directional first-order Gaussian steerable filters are constructed to convolve the image, and by pooling the convolution results to achieve feature dimensionality reduction, a multi-source consistent feature description is constructed. Finally, feature matching is performed based on the nearest-neighbor principle, and high-precision correspondences are obtained by eliminating mismatches. And bundle adjustment is further performed based on the rational function model to calibrate the rational polynomial coefficients of the images to be aligned and geometrically correct the images, so as to realize the fine registration of the images. Results and Conclusions: Experimental results using multiple pairs of satellite multisource images show that the accuracy of the proposed method is better than 1 pixel on multitemporal optical data, optical-infrared data, and 1.5 pixels on optical-synthetic aperture radar data, and the computational efficiency is more than doubled compared to the existing similar methods. [ABSTRACT FROM AUTHOR]

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Objectives: The registration of remote sensing image data and GIS ( geographic information system ) vector data is the basis of the integration of remote sensing and GIS, which is widely used in the fields of map data update, city monitoring, map change detection and so on. At present, the key to the registration of remote sensing images and vector data is the extraction of remote sensing image features. However, the existing remote sensing image feature extraction has problems such as incomplete feature extraction, which leads to registration failure or low accuracy. This paper proposes a registration method for remote sensing images and vector data based on Mask region -based convolutional neural network (Mask R -CNN). Methods: Firstly, we select the road intersection as the distinctive feature of the same name in the remote sensing image and vector data, and create a road intersection image data set to train Mask R -CNN model. Secondly, according to the geometric topological relationship, the vector data road intersections are selected as vector control points. And take the intersection control point of the vector data as the center, we use 400 × 400 pixels window to crop the remote sensing image data and input it into the Mask R-CNN model, extract the border of the road intersection in the image. The control points of the same name are determined according to the Euclidean distance between the remote sensing image and the vector data control points, and the control points of the same name are cleaned using the density-based spatial clustering of applications with noise algorithm. Finally, the affine transformation parameters are calculated according to the filtered control points of the same name to realize the registration of remote sensing image and vector data. The registration data of Shanghai vector data and Gaofen-2 image data were selected for registration experiment. Results: Experimental results show that the average deviations of the experimental data before were 15. 34 m and 1. 44 m, before and after registration based on Mask R -CNN. This method can correctly register remote sensing images and vector data, and the proposed method has better application prospects in urban data registration, and has the characteristics of strong robustness and high accuracy. Conclusions: The proposed method in this paper can automatically register remote sensing images and vector data from different sources in areas such as mountains, grasslands, and deserts. In the next step, the amount of sample data of remote sensing images will be increased, including types of remote sensing images and types of features. [ABSTRACT FROM AUTHOR]

Copyright of Geomatics & Information Science of Wuhan University is the property of Geomatics & Information Science of Wuhan University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Complex image registration is a crucial step in interferometric synthetic aperture sonar (InSAS) signal processing. The registration quality directly influences the generation of interferogram and subsequent reconstruction of digital elevation model (DEM). Utilizing the geometric model of imaging, the rational function relationship between offset and slant range is deduced, thus this paper propose a new InSAS complex image registration method based on second order rational function surface fitting. Compared with the traditional polynomial surface fitting method, the proposed method in this paper has the advantages of higher precision and smaller calculation cost. The evaluation has been carried out using the root mean squared error(RMSE), correlation coefficient,residue numbers and computing time as the criteria. Simulation and real data experiments results indicate the validity of the proposed method. [ABSTRACT FROM AUTHOR]

Objectives：To address significant geometric deformation and radiometric differences between optical and synthetic aperture radar (SAR) images, this paper proposes an automatic registration method based on spatial geometry constraint and structure feature. Methods：Firstly, the Harris detector with a block strategy is used to extract evenly distributed feature points in the input images. Subsequently, a local geometric correction is performed for the input image by using rational function model, which aims to achieve local coarse registration between the input image and the reference image. Then, the geometric structural feature descriptor is constructed by using orientated gradient information of images, and the feature descriptor is transformed into the frequency domain, the phase correlation is used as the similarity metric to achieve correspondences by employing a template matching strategy. Finally, the least square method is used to eliminate the mismatches based on spatial geometric constraint relationship between images, followed by a process of geometric correction to achieve the image registration. Results and Conclusions： Three sets of high⁃resolution optical and SAR images were selected as the experimental data. The results the proposed outperform traditional methods in both matching performance and computational efficiency. The proposed method can achieve the registration of optical and SAR effectively and accurately. [ABSTRACT FROM AUTHOR]

In this paper, a method of vehicular point cloud data enhancement with panoramic image is proposed. Firstly, the classification of point cloud data and the extraction of single object interested are realized by combining DBSCAN （density-based spatial clusterig of applications with noise） segmentation algorithm and typical features of objects; then, the missing area is detected and the corresponding edge is extracted one by one for a point cloud of single object; finally, a region growth dense matching based on panoramic image with local affine transform is proposed. The method is used to generate the 3D point data in the missing area, and to enhance the point cloud data. Experiment results show that this method can fill the missing area of cloud data in streetscape, and the result of point cloud enhancement is real and reliable. [ABSTRACT FROM AUTHOR]

Automatic water-body extraction from remote sensing images is a challenging problem. In this paper, a novel automatic water-body extraction technique is proposed for optical visible remote sensing images. It integrates image segmentation, image registration and change detection with GIS data as a whole process. A new iterative segmentation and registration strategy is also proposed. A multi-scale visual attention model is introduced to detect salient areas and a level-set segmentation algorithm is employed for image segmentation. An improved shape curve similarity (ISCS) method is presented to constrain the matching of image segmentation objects and GIS-identified water-bodies. Furthermore, a buffer-based change detection algorithm was designed to obtain unchanged water-bodies and non-water objects were eliminated with the aid of GIS data and spectral features. Experiments were carried out on three sets of data. Results show that the proposed method was effective in rapid water body extraction and change detection. [ABSTRACT FROM AUTHOR]