Automated registration of hyperspectral images for precision agriculture

Erives, Hector; Fitzgerald, Glenn J.

doi:10.1016/j.compag.2004.11.016

Cited by 37 publications

(15 citation statements)

References 4 publications

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“…), 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

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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

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Section: Introductionmentioning

confidence: 99%

Automatic Descriptor-Based Co-Registration of Frame Hyperspectral Data

Vakalopoulou

Καράντζαλος

2014

Remote Sensing

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“…HS imaging has found its application in many scientific fields [1], such as "precision farming" and identification of different types or diseases of crops in agriculture [2][3][4][5]. Diagnosis of cancers by means of spectroscopic inspection of tissue surfaces [6,7] and measuring the saturation of hemoglobin oxygen in the skin [8] are some of the medical application of this technique.…”

Section: Hyperspectral Imagerymentioning

confidence: 99%

Superresolution of hyperspectral images using backpropagation neural networks

Mianji

Zhang

Babakhani³

2009

2009 2nd International Workshop on Nonlinear Dynamics and Synchronization

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Hyperspectral technology has introduced a new perspective in remote sensing applications but suffers from low spatial resolution. A new spatial-spectral data fusion technique based on spectral mixture analysis and super-resolution mapping for spatial resolution enhancement of hyperspectral imagery is proposed in this paper. To this end a linear mixture model and a fully constrained least squares based unmixing algorithm are applied for spectral unmixing of the hyperspectral imagery and the resulted fractional images are processed based on a spatial-spectral information correlation model through a super-resolution mapping technique. To validate the performance of the method, experiments are carried out on real images. The obtained results validate the effectiveness of the method. It doesn't need any a priori information of the scene or secondary high resolution source of data, and is low in terms of computational cost.

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“…Applications of this method can be found in agriculture [10], where we reported using the PC method to successfully register hyperspectral image cubes to an accuracy of ± 1 pixel. In this paper we report results obtained with an enhanced FM method that allowed us to achieve sub-pixel registration accuracy.…”

Section: Introductionmentioning

confidence: 96%

Automatic Sub-pixel Registration for a Tunable Hyperspectral Imaging System

Erives

Fitzgerald

2006

2006 IEEE International Symposium on Geoscience and Remote Sensing

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Hyperspectral imagery of the Earth's surface is increasingly being acquired from aerial platforms. The many bands acquired by typical hyperspectral instruments are collected either in a push-broom, scanning, or staring fashion. Staring methods can be used in ground and aerial based applications, and have the advantage of readily producing coherent images. Staring remote sensing instruments need some form of co-registration to match band-to-band pixel locations, because it takes some time for the instrument to acquire images and save them as the aerial platform moves above the target scene. A well known method for registration is the Phase Correlation (PC) which may be used to register images to an accuracy of ±1 pixel. In this paper we report an enhancement to the PC method that allows for sub-pixel registration of hyperspectral images. The x-y location at which the maximum correlation function occurs is fitted with a cubic interpolation to find the maximum. This method was implemented to recover sub-pixel rotation and translation accuracy from an airborne hyperspectral imaging instrument, dubbed the portable hyperspectral tunable imaging system. Results showed that the approach improves up to 9.5 % of the normalized cross correlation between wavebands in comparison with the PC method alone.

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Automated registration of hyperspectral images for precision agriculture

Cited by 37 publications

References 4 publications

Automatic Descriptor-Based Co-Registration of Frame Hyperspectral Data

Automatic Descriptor-Based Co-Registration of Frame Hyperspectral Data

Superresolution of hyperspectral images using backpropagation neural networks

Automatic Sub-pixel Registration for a Tunable Hyperspectral Imaging System

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