Abstract
Abstract. Reconstructing the 3D profile from a set of UAV-based images can obtain hyperspectral information, as well as the 3D coordinate of any point on the profile. Our images are captured from the Cubert UHD185 (UHD) hyperspectral camera, which is a new type of high-speed onboard imaging spectrometer. And it can get both hyperspectral image and panchromatic image simultaneously. The panchromatic image have a higher spatial resolution than hyperspectral image, but each hyperspectral image provides considerable information on the spatial spectral distribution of the object. Thus there is an opportunity to derive a high quality 3D point cloud from panchromatic image and considerable spectral information from hyperspectral image. The purpose of this paper is to introduce our processing chain that derives a database which can provide hyperspectral information and 3D position of each point. First, We adopt a free and open-source software, Visual SFM which is based on structure from motion (SFM) algorithm, to recover 3D point cloud from panchromatic image. And then get spectral information of each point from hyperspectral image by a self-developed program written in MATLAB. The production can be used to support further research and applications.
Highlights
Hyperspectral images can be used for finding objects, identifying materials, or detecting spatial processes
Most of the researches focus on the integration of spectral data into 3D information, while the 3D profile are generated by active methods, such as Lidar
In this study we describe a novel method to derive hyperspectral information and 3D information from images captured by UHD hyperspectral camera based on structure from motion (SFM)
Summary
Hyperspectral images can be used for finding objects, identifying materials, or detecting spatial processes. The hyperspectral image contains continuous hundreds bands of the electromagnetic spectrum in the sensor’s wavelength range It can provide considerable information on the spatial spectral distribution of the object. 3D reconstruction is the process of recovering the shape and profile of real objects This process can be accomplished either by active or passive methods. Active methods derive the depth map using methods of structure light, laser range finder and radiometric rangefinders, reconstruct the 3D profile by numerical approximation approach and establish the object in the scene based on model. This method can get high quality data, it requires expensive equipment. This is a challenging problem when integrating data from different sensors, at different times, or possibly mounted on different platforms
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