Hector Erives

Implementation of a 3-D Hyperspectral Instrument for Skin Imaging Applications

The many bands acquired by typical multispectral, hyperspectral, and ultraspectral instruments are collected in either a scanning or staring fashion. Staring instruments, like that described in this paper, are popular because they are capable of producing spatially coherent images of a target scene; therefore, they are suitable for noncontact inspection applications. However, the wavebands need to be coregistered. A new application of hyperspectral instrumentation is proposed, where a sheet-of-light method, produced by a low-power laser light, is used to compute range measurements, and a hyperspectral instrument is used to acquire spectral information in the visible (VIS) range of the spectrum. The main advantage of this method is that a single hyperspectral tunable filter arrangement is used to sweep the image to get 3-D information and acquire hyperspectral spectral measurements of a scene. This paper describes the implementation of the image acquisition subsystem, which was done in LabView, and the method used to obtain coregistered radiometrically calibrated spectral imagery and range information. Test cases showed that the instrument may be used to retrieve height and spectral information of small areas and could be used for skin-imaging applications.

Automatic subpixel registration for a tunable hyperspectral imaging system

Hyperspectral imagery of the Earths 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 coregistration 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 plusmn1 pixels. This letter reports an enhancement to the PC method that allows for subpixel 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 subpixel rotation and translation accuracy from an airborne hyperspectral imaging system, 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

An Automated Nonrigid Registration for a Tunable Hyperspectral Imaging System

A method that uses the Phase Correlation and a geometric transformation is proposed to estimate nonrigid registration errors for a hyperspectral imaging system. It computes multiple correlations to find and correct for local registration errors.

UAV Image Segmentation Using a Pulse-Coupled Neural Network for Land Analysis

This chapter presents a pulse-coupled neural network architecture, PCNN, to segment imagery acquired with UAV images. The images correspond to normalized difference vegetation index values. The chapter describes the image analysis system design, the image acquisition elements, the original PCNN architect, the simplified PCNN, the automatic parameter setting methodology, and qualitative and quantitative results of the proposed method using real aerial images.

Low complexity dimensionality reduction for hyperspectral images

In hyperspectral imaging systems, principal component analysis (PCA), also known as the Karhunen Loeve Transform (KLT) is the conventional way of spectral dimensionality reduction which compacts the image energy into relatively few coefficients to enable compression. The computational burden of the data dependent PCA/KLT often exceeds the capacity of resource constrained hyperspectral sensing platform considering the large size of the hyperspectral image. We propose to use a spectral dimensionality reduction method based on the relationship between KLT and the Discrete Prolate Spheroidal Sequences (DPSS). DPSSs construct a highly efficient basis that captures most of the signal energy while in signal processing the KLT is used to find the filter that maximizes the concentration of the output energy for a given spectrum of the input signal. On the other hand, spatial dimensionality reduction can provide significant amount of reduction as well. The reduction in the spatial domain can be implemented by subsampling. We demonstrate our methods performance on the AVIRIS Hyperspectral data.

Implementation of a VIS/NIR 3D Hyperspectral Instrument For In Vivo Imaging

A novel application of hyperspectral instrumentation is described here, where a sheet-of-light method, produced by a low power laser light, is used to compute range measurements, and a hyperspectral instrument is used to acquire spectral information in the visible and near-infrared range of the spectrum. This report addresses two problems which in the literature are generally addressed independently; acquisition and analysis of hyperspectral images, and acquisition and analysis of range information. The bimodal instrument described in this report consists of a LCTF-based hyperspectral system which is used to acquire spectral images in the 450-1100 nm range and a multi-line laser light used to acquire range measurements of a scene. This laser light method can be used to acquire fast range measurements as the scene is partitioned into three sub-ranges therefore reducing the acquisition time by threefold. The methods used to get calibrated hyperspectral measurements (to reflectance values) of the proposed 3D hyperspectral instrument, and range measurements (to mm) are described in this paper. A test case shows the capabilities of this instrument for producing 3D hyperspectral imagery of human skin samples.

Automatic Sub-pixel Registration for a Tunable Hyperspectral Imaging System

Hyperspectral imagery of the Earths 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 plusmn1 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.

Automated registration of hyperspectral images

Hyperspectral images of the Earth’s surface are increasingly being acquired from aerial platforms. The dozens or hundreds of bands acquired by a typical hyperspectral sensor are acquired either through a scanning process or by collecting a sequence of images at varying wavelengths. This latter method has the advantage of acquiring coherent images of a scene at different wavelengths. However, it takes time to collect these images and some form of co-registration is required to build coherent image cubes. In this paper, we present a method to register many bands acquired sequentially at different wavelengths from a helicopter. We discuss the application of the Phase Correlation (PC) Method to recover scaling, rotation, and translation from an airborne hyperspectral imaging system, dubbed PHyTIS. This approach is well suited for remotely sensed images acquired from a moving platform, which induces image registration errors due to along and across track movement. We were able to register images to within ± 1 pixel across entire image cubes obtained from the PHyTIS hyperspectral imaging system, which was developed for precision farming applications.