Non-rigid registration of hyperspectral imagery for analysis of agronomic scenes

Erives, Hector; Fitzgerald, Glenn J.; Clarke, Thomas R.

doi:10.1016/j.biosystemseng.2007.08.006

Cited by 2 publications

(1 citation statement)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…), and the distortions that are induced by the multi-filter optical system. As a consequence, the registration process should be able to cope with transformations that are parametrized by an increased number of degrees of freedom, such as the affine and the polynomial ones [8,9].…”

Section: Introductionmentioning

confidence: 99%

Automatic Descriptor-Based Co-Registration of Frame Hyperspectral Data

Vakalopoulou

Καράντζαλος

2014

Remote Sensing

View full text Add to dashboard Cite

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

show abstract

Section: Introductionmentioning

confidence: 99%