Ebadat G. Parmehr

An effective histogram binning for mutual information based registration of optical imagery and 3D LiDAR data

Automatic registration of multi-sensor data is a basic step in data fusion applications. Mutual information (MI) has been widely used in medical and remote sensing image registration. In this paper, an effective histogram binning technique is proposed to improve the robustness of image registration using MI and Normalized MI (NMI). Increasing the bin size improves the robustness of MI to local maxima that occur in the convergence surface of MI. In addition, the computation cost of registration is decreased due to use of a smaller joint pdf, without decreasing the accuracy. The performance of the proposed method in the registration of aerial imagery with LiDAR data has been experimentally evaluated and the results obtained are presented.

Automatic co-registration of satellite imagery and LiDAR data using local Mutual Information

Automatic co-registration is a basic step in multi-sensor data fusion for remote sensing applications. The effectiveness of Mutual Information (MI) as a similarity measure for multi-sensor image registration has previously been reported for medical and remote sensing applications. In this paper, a new intensity-based approach built on local MI principles is presented. The approach decreases the complexity of higher dimension optimization by measuring local MI on well-distributed tie points. In addition, the reliability of registration is improved due to utilization of redundant observations of similarity. The performance of the proposed method for the registration of WorldView2 satellite imagery with LiDAR elevation and intensity data has been experimentally evaluated and the results obtained are presented.

Automatic Registration of Aerial Images with 3D LiDAR Data Using a Hybrid Intensity-Based Method

Automatic image registration is a basic step in multi-sensor data integration in remote sensing and photogrammetric operations such as data fusion. The effectiveness of intensity-based methods for automated multi-sensor image registration, such as Mutual Information (MI) and the Correlation Ratio (CR), have previously been demonstrated for medical and remote sensing applications. In this paper, a new hybrid intensity-based approach that utilizes both statistical and functional relationships between images, particularly in the case of registering aerial images and 3D point clouds, is presented. The performance of the proposed method for the registration of aerial orthoimagery and LiDAR range and intensity data has been experimentally evaluated and the results obtained are presented.

Automatic Parameter Selection for Intensity-Based Registration of Imagery to LiDAR Data

Automatic registration of multisensor data, for example, imagery and Light Detection And Ranging (LiDAR), is a basic step in data fusion in the field of geospatial information processing. Mutual information (MI) has recently attracted research attention as a statistical similarity measure for intensity-based registration of multisensor images in the related fields of computer vision and remote sensing. Since MI-based registration methods rely on joint probability density functions (pdfs) for the data sets, errors in pdf estimation can affect the MI value, causing registration failure due to the presence of nonmonotonic surfaces of similarity measure. The quality of the estimated pdf is highly dependent upon both the bin size and the smoothing technique used in the pdf estimation procedure. The lack of a general approach to assign an appropriate bin size value for the pdf of multisensor data reduces both the level of automation and the robustness of the registration. In this paper, a novel bin size selection approach is proposed to improve registration reliability. The proposed method determines the best (uniform or variable) bin size for the pdf estimation via an analysis of the relationship between the similarity measure values of the data and the adopted geometric transformation. This highlights the role of the component of MI sensitive to the transformation, rather than the MI component that is unrelated to the transformation, such as noise. The performance of the proposed method for the registration of aerial imagery to LiDAR point clouds is investigated, and experimental results are compared with those achieved through a feature-based registration method.