Line-Based Registration of DSM and Hyperspectral Images

Abstract

Data fusion techniques require a good registration of all the used datasets. In remote sensing, images are usually geo-referenced using the GPS and IMU data. However, if more precise registration is required, image processing techniques can be employed. We propose a method for multi-modal image coregistration between hyperspectral images (HSI) and digital surface models (DSM). The method is divided in three parts: object and line detection of the same object in HSI and DSM, line matching and determination of transformation parameters. Homogeneous coordinates are used to implement matching and adjustment of transformation parameters. The common object in HSI and DSM are building boundaries. They have apparent change in height and material, that can be detected in DSM and HSI, respectively. Thus, before the matching and transformation parameter computation, building outlines are detected and adjusted in HSI and DSM. We test the method on a HSI and two DSM, using extracted building outbounds and for comparison also extracted lines with a line detector. The results show that estimated building boundaries provide more line assignments, than using line detector.

Coregistration refinement of hyperspectral images and DSM: An object-based approach using spectral information

“…A manually selection of tie points from in the HSI is insufficient for computing a reference transformation matrix (Avbelj et al, 2013). The position of a manually set tie point in a HSI strongly depends on the contrast and selection of the channels from the HSI.…”

“…This article extends the coregistration refinement method for HSI and DSM in urban areas by Avbelj et al (2013). The flowchart describing the proposed object-based coregistration method is shown in Fig.…”

“…Many building detection and modelling methods have been proposed in the last decades, using DEM or LiDAR point clouds (e.g., Maas and Vosselman, 1999;Lafarge et al, 2008), or using additionally optical imagery (e.g., Sohn and Dowman, 2007;Awrangjeb et al, 2010). In this subsection, we summarize the building outline detection method described in Avbelj et al (2013), but any other building outline detection could be used. The method can be applied on a HSI and a DSM and is divided in three steps: (a) building mask extraction, (b) building model selection, and (c) boundary adjustment.…”

Method for Creating Digital Orthophotomaps of Increased Informational Value

“…Some authors focus their attention on the methods of complex application of LIDAR data, and multi-spectral photography as well as aerial surveying of high geometric differentiation in RGB format with spectral data of distant probing. The first sources supply the development of a high quality geometric model, and the latter are needed for the detection of a type of object according to spectral peculiarities of its depiction [Acar et al 2017;Avbelja et al 2013]. Ok [2009] proposes to use vegetation indexes, measured according to RGB depiction, for the classification of vegetation and man-made layers in order to use only the images, and no other sources of data.…”

Hyperspectral imagery superresolution

“…HS fusion problem can be roughly categorized into two classes: Pan-sharpening Based Methods and Subspace Based Methods. It is important to say that image fusion require a good registration, however, in remote sensing images are generally geo-referenced using GPS and IMU data[79]. Therefore, there is no need to apply registration unless more precise registration is required.…”