Coarse-to-Fine Registration of Airborne LiDAR Data and Optical Imagery on Urban Scenes

Abstract

Applications based on synergistic integration of optical imagery and LiDAR data are receiving a growing interest from the remote sensing community. However, a misaligned integration of these datasets fails to fully profit from the potential of both sensors. An optimum fusion of optical imagery and LiDAR data requires an accurate registration. This is a complex problem since a versatile solution is still missing, especially when data are collected at different times, from different platforms, under different acquisition configurations. This article presents a coarse-to-fine registration method of optical imagery with airborne LiDAR data acquired in such context. First, a coarse registration involves processes of extraction and matching of building candidates from the two datasets. Then, a mutual-information-based fine registration is carried out. It involves a superresolution approach applied to LiDAR data to generate images with the same resolution as the optical image, and a local approach of transformation model estimation. The proposed method succeeds at overcoming the challenges associated with this difficult context. For instance, considering the experimented airborne LiDAR (2011) and orthorectified aerial imagery (2016) datasets, their spatial shift is reduced by 48.15% after the proposed coarse registration. Moreover, the incompatibility of size and spatial resolution is well addressed by the superresolution. Finally, a high accuracy of dataset alignment is also achieved, highlighted by a 40-cm error based on a checkpoint assessment and a 64-cm error based on a check-pair-line assessment. These promising results enable further researches for a complete fusion methodology between these datasets in this challenging context.

Super-Resolution-Based Snake Model—An Unsupervised Method for Large-Scale Building Extraction Using Airborne LiDAR Data and Optical Image

“…As the two data sources are used jointly, a registration is necessary in order to avoid misalignment problems. This registration can be carried out a priori (i.e., data acquisition using the same platform) or a posteriori [47,48]. It aims to estimate the transformation model, allowing reducing the misalignment between the two datasets.…”

“…During the SR process, φ is vectorized into a column vector of n = n x × n y elements. The set of transformation model parameters θ results from the registration [48]. It aims to define the projection of 3-D points onto the image space.…”

“…FISTA is significantly faster than standard gradient-based methods such as Iterative Shrinkage-Thresholding algorithms (ISTA). Full details on the implementation of the proposed SR process can be found in [48].…”

A Novel Speckle Suppression Method with Quantitative Combination of Total Variation and Anisotropic Diffusion PDE Model

“…There are many granular specks distributed on the image, which is called speckle noise. Speckle is commonly interpreted as a kind of locally correlated noise that reduces image contrast and conceals fine feature details, causing negative effects on target detection and recognition [2,3] scene segmentation [4], and image registration [5]. In consideration of the damaging effect of speckle on images, speckle suppression is required to smooth uniform areas of the images and preserve the features, like edges and textures.…”

Coregistration of Remote Sensing Image Based on Histogram Kernel Predictability

“…Another line of research that has aroused much interest is the co-registration of multimodal images and Light Detection and Ranging (LiDAR) data. For example, in [17], the authors proposed a methodology for registering aerial images and LiDAR; in this work, the authors make efficient use of the structural information through Gabor filters; in the case of MI and NMI, a variety of works address this problem [18], [19]. Even there exist algorithms that use MI and deep learning to register point clouds [20], or applications of MI for calibrating LiDAR and cameras [21].…”