<title>Spectral imaging applications: remote sensing, environmental monitoring, medicine, military operations, factory automation, and manufacturing</title>

Abstract

This paper reviews the activities at OKSI related to imaging spectroscopy presenting current and future applications of the technology. We discuss the development of several systems including hardware, signal processing, data classification algorithms and benchmarking techniques to determine algorithm performance. Signal processing for each application is tailored by incorporating the phenomenology appropriate to the process, into the algorithms. Pixel signatures are classified using techniques such as principal component analyses, generalized eigenvalue analysis and novel very fast neural network methods. The major hyperspectral imaging systems developed at OKSI include the Intelligent Missile Seeker (IMS) demonstration project for real-time targetldecoy discrimination, and the Thermal InfraRed Imaging Spectrometer (TIRIS) for detection and tracking of toxic plumes and gases. In addition, systems for applications in medical photodiagnosis, manufacturing technology, and for crop monitoring are also under development.Keywords: imaging spectroscopy, multispectral, hyperspectral, applications, sensors, algorithms, medical imaging, remote sensing, crop monitoring, machine vision, infitwed, spectral unmixing, classification, phenomenology. I THE TECHNOLOGYImaging spectroscopy is a combination of several photonic technologies. It combines conventional imaging with spectroscopy and radiometry to produce images for which a spectral signature is associated with each spatial resolution element (pixel). Hence, data produced by a spectral imager constitute a 3-dimensional cube with 2 spatial and a third spectral dimension, as depicted in Fig. 1. Intensity values of the data cube can be converted, via proper calibration, to radiometric quantities that are related to the scene phenomenology. The position of spectral imaging relative to related technologies is shown in Fig. 2. The power of the technique is that the phenomenology provides a link to spatial and spectral analytical models, spectral libraries, etc., to support various applications.It is obvious why the combination of imaging and spectroscopy is very attractive. In principle, one can improve image understanding by combining the best of two worlds: spatial and spectra2 analyses. The combined analysis may be considered as data fusion. Conventional color imagery 1 submitted manuscript has been authored by a contractor of the U.S.

Spatially Enhanced Spectral Unmixing Through Data Fusion of Spectral and Visible Images from Different Sensors

“…This amount of valuable information can be used in critical image-based geoscience applications [3]. Unfortunately, the spatial resolution (SR) of remotely sensed spectral images is low, thus limiting the accuracy of the outcomes of spectral data applications [4][5][6][7], e.g., classification, unmixing, target and object detection, mineralogy, and change detection.…”

High-speed hyperspectral video acquisition with a dual-camera architecture

“…Thanks to its capability for detailed scene representation, this technique has been widely adopted in many fields, including medical diagnosis, health care, remote sensing, and military operations [17,24]. Recently, it is found that various computer vision tasks, e.g., recognition, classification, and tracking, can benefit from incorporating the spectral information in tens or hundreds of bands [11,20,28].…”

Hyperspectral imaging for diagnosis and quality control in agri-food and industrial sectors and tissue biopsy

“…Se puede solventar sin más que ubicar el sistema verticalmente y desplazar el material bajo el mismo en una sola dirección, tal y como hace una cinta transportadora en una planta de producción industrial. El empleo de la espectroscopia de imagen procede de aplicaciones militares y de sensado remoto [3]. Sin embargo, debido a sus características de operación no intrusivas ni destructivas, se preveía que podía dar solución a multitud de problemas en diferentes sectores industriales (agroalimentario, farmacológico, químico, etc.)…”

“…Sin embargo, debido a sus características de operación no intrusivas ni destructivas, se preveía que podía dar solución a multitud de problemas en diferentes sectores industriales (agroalimentario, farmacológico, químico, etc.) mejorando notablemente sus rendimientos económicos [3]. En aplicaciones industriales se utilizan tradicionalmente técnicas de visión o instrumentos que realizan medidas espectrales de manera puntual [4].…”