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United States Department of Agriculture

Agricultural Research Service


item Erives, Hector
item Fitzgerald, Glenn
item Clarke, Thomas

Submitted to: Biosystems Engineering
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/7/2007
Publication Date: 12/12/2007
Citation: Erives, H., Fitzgerald, G.J., Clarke, T.R. 2007. Non-rigid registration of hyperspectral imagery for analysis of agronomic scenes. Biosystems Engineering. 98:267-275

Interpretive Summary: Aerial images of agricultural fields are useful tools for farmers in managing their crops. Recent advances in optical electronics allow these images to be collected in many narrow wavelengths in rapid succession, greatly increasing their analytical value. However, the image shifts slightly between wavelengths, due to aircraft motion. Not only is the forward motion of the aircraft a problem, but slight shifts in attitude and optics can cause considerable distortion as well. The many images have to be aligned precisely before any analysis can be performed, and this process can be so time-consuming and expensive as to negate any increased value from the imagery. This article describes a new method that automatically corrects both kinds of errors, allowing rapid image analysis and therefore greatly increasing the usefulness of aerial imagery for farm management.

Technical Abstract: Analysis of remote sensing imagery usually entails the registration of images from different multiple wavelengths. Even though a staring instrument has the advantage of readily producing coherent spectral images, often these images still need some form of band-to-band registration to correct for instrument movement, pixel shift caused by vibration at different acquisition times, or optical distortion introduced by a difference in optics. In this study we report a method to register hyperspectral images. The method consists of partitioning the pair of registering images (from different wavelentghs), into multiple overlapping regions of interest (ROIs) and finding registration errors in each of these areas using the standard Phase Correlation (PC). The registration errors from the ROIs are then used to compute a geometric transformation, which is applied to the entire image to correct for non-rigid image registration errors that are mostly due to optical effects. This technique was tested on hyperspectral imagery acquired by a portable hyperspectral tunable imaging system developed by the U.S. Arid-Land Agricultural Research Center (USALARC) for ground and remote sensing applications that include vegetative analysis for precision agriculture.

Last Modified: 06/27/2017
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