Automatic Descriptor-Based Co-Registration of Frame Hyperspectral Data

Abstract

Frame hyperspectral sensors, in contrast to push-broom or line-scanning ones, produce hyperspectral datasets with, in general, better geometry but with unregistered spectral bands. Being acquired at different instances and due to platform motion and movements (UAVs, aircrafts, etc.), every spectral band is displaced and acquired with a different geometry. The automatic and accurate registration of hyperspectral datasets from frame sensors remains a challenge. Powerful local feature descriptors when computed over the spectrum fail to extract enough correspondences and successfully complete the registration procedure. To this end, we propose a generic and automated framework which decomposes the problem and enables the efficient computation of a sufficient amount of accurate correspondences over the given spectrum, without using any ancillary data (e.g., from GPS/IMU). First, the spectral bands are divided in spectral groups according to their wavelength. The spectral borders of each group are not strict and their formulation allows certain overlaps. The spectral variance and proximity determine the applicability of every spectral band to act as a reference during the registration procedure. The proposed decomposition allows the descriptor and the robust estimation process to deliver numerous inliers. The search space of possible solutions has been effectively narrowed by sorting and selecting the optimal spectral bands which under an unsupervised manner can quickly recover hypercube's geometry. The developed approach has been qualitatively and quantitatively evaluated with six different datasets obtained by frame sensors onboard aerial platforms and UAVs. Experimental results appear promising.Remote Sens. 2014, 6 3410

Compact Hyperspectral Imaging System (Cosi) for Small Remotely Piloted Aircraft Systems (Rpas) – System Overview and First Performance Evaluation Results

“…Analysis of the band co-registration quality using template matching and normalized cross-correlation on nine reference points showed that the average RMSE between the spectral bands and the reference band 31 is equal to 0.25pix. This result is similar to the one recently obtained for the Rikola DT-0014 camera and reported in (Tommaselli et al, 2015) and better than numbers reported in (Vakalopoulou and Karantzalos, 2014). Although the obtained result is considered as of high quality, the vectors of extreme 2D residuals between some bands can get close to 1pix ( Figure 10) and thus can influence spectral measurements (similar conclusion was made in (Tommaselli et al, 2015) for the Rikola camera).…”

“…plant height in vegetation growth monitoring. In the explored dataset geometric band co-registration is comparable or better than corresponding values reported in literature for data captured by the Rikola camera (Tommaselli et al, 2015;Vakalopoulou and Karantzalos, 2014). The obtained results are very encouraging, nonetheless evaluation of more datasets covering different environment is required.…”

Sensor Pods: Multi-Resolution Surveys from a Light Aircraft

“…The OCI UAV1000 recorded continuously in pushbroom imaging mode from the beginning of each flight line and therefore differs in manner of image capture to the other sensors in the sensor pod and existing RPAS dedicated hyperspectral frame sensors [45]. Exposure, frames per second and shutter speed were calculated and correctly specified for the flying height and speed to avoid blurring or other problems during the subsequent image matching stage.…”

Multimodal Data Fusion for Effective Surveillance of Critical Infrastructure

“…The developed system integrates a number of software modules like dimensionality reduction algorithms (Karantzalos 2009), registration , (Vakalopoulou and Karantzalos, 2014), background subtraction, moving objection detection and calculation of optical flows, velocity/ density/ direction (Kandylakis et al, 2015). Moreover, certain software modules are responsible for performing scene classification tasks based on recent approaches like in (Makantasis et al, 2015).…”