1,384 research outputs found

    Computationally Efficient Target Classification in Multispectral Image Data with Deep Neural Networks

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    Detecting and classifying targets in video streams from surveillance cameras is a cumbersome, error-prone and expensive task. Often, the incurred costs are prohibitive for real-time monitoring. This leads to data being stored locally or transmitted to a central storage site for post-incident examination. The required communication links and archiving of the video data are still expensive and this setup excludes preemptive actions to respond to imminent threats. An effective way to overcome these limitations is to build a smart camera that transmits alerts when relevant video sequences are detected. Deep neural networks (DNNs) have come to outperform humans in visual classifications tasks. The concept of DNNs and Convolutional Networks (ConvNets) can easily be extended to make use of higher-dimensional input data such as multispectral data. We explore this opportunity in terms of achievable accuracy and required computational effort. To analyze the precision of DNNs for scene labeling in an urban surveillance scenario we have created a dataset with 8 classes obtained in a field experiment. We combine an RGB camera with a 25-channel VIS-NIR snapshot sensor to assess the potential of multispectral image data for target classification. We evaluate several new DNNs, showing that the spectral information fused together with the RGB frames can be used to improve the accuracy of the system or to achieve similar accuracy with a 3x smaller computation effort. We achieve a very high per-pixel accuracy of 99.1%. Even for scarcely occurring, but particularly interesting classes, such as cars, 75% of the pixels are labeled correctly with errors occurring only around the border of the objects. This high accuracy was obtained with a training set of only 30 labeled images, paving the way for fast adaptation to various application scenarios.Comment: Presented at SPIE Security + Defence 2016 Proc. SPIE 9997, Target and Background Signatures I

    Maintenance and operation of the multispectral data collection and reproduction facilities of the Willow Run Laboratories

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    The accomplishments in multispectral mapping during 1970 and (fiscal year) 1971 are presented. The mapping was done with the instrumented C-47 aircraft owned and operated by Willow Run Laboratories of The University of Michigan. Specific information for flight operations sponsored by NASA/MSC (Manned Spacecraft Center) in 1970 and fiscal year 1971 is presented, and a total listing of flights for 1968, 1969, 1970, and fiscal year 1971 is included in the appendices. The data-collection and reproduction facilities are described

    The design of a Space-borne multispectral canopy LiDAR to estimate global carbon stock and gross primary productivity

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    Understanding the dynamics of the global carbon cycle is one of the most challenging issues for the scientific community. The ability to measure the magnitude of terrestrial carbon sinks as well as monitoring the short and long term changes is vital for environmental decision making. Forests form a significant part of the terrestrial biosystem and understanding the global carbon cycle, Above Ground Biomass (AGB) and Gross Primary Productivity (GPP) are critical parameters. Current estimates of AGB and GPP are not adequate to support models of the global carbon cycle and more accurate estimates would improve predictions of the future and estimates of the likely behaviour of these sinks. Various vegetation indices have been proposed for the characterisation of forests including canopy height, canopy area, Normalised Difference Vegetation Index (NDVI) and Photochemical Reflectance Index (PRI). Both NDVI and PRI are obtained from a measure of reflectivity at specific wavelengths and have been estimated from passive measurements. The use of multi-spectral LiDAR to measure NDVI and PRI and their vertical distribution within the forest represents a significant improvement over current techniques. This paper describes an approach to the design of an advanced Multi-Spectral Canopy LiDAR, using four wavelengths for measuring the vertical profile of the canopy simultaneously. It is proposed that the instrument be placed on a satellite orbiting the Earth on a sun synchronous polar orbit to provide samples on a rectangular grid at an approximate separation of 1km with a suitable revisit frequency. The systems engineering concept design will be presented

    Simultaneous spectral imaging at several excitation and emission wavelengths

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    The inability to complete removal of a tumor during an ontological surgery results in an increase in the rate of recurrence of the tumor and thus, in a repeated surgery. The way to minimize that risk depends mainly on: getting the precise tumor localization, being able to identify it in the molecular level identification and the capability to distinguish the tumor from the non-malignant tissue that surrounds it. Although in last years new techniques for localization pre-operative and intra-operative have been developed, improved and introduced, identification and distinction methods continue being a considerable problem during the surgery. To overcome this challenges, we have developed a real-time fluorescence endoscope that provide the surgeon the capability to distinguish accurately the malignant tissue from the healthy one. For that, the use of an specific tumour-targeted bio-marker able to identify cancer cells and to bind them, thus producing fluorescence only in the determined malignant region. This fluorescence light emitted is detected by the camera and then processed to highlight the fluorescence.Ingeniería Biomédic

    Index to NASA Tech Briefs, 1975

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    This index contains abstracts and four indexes--subject, personal author, originating Center, and Tech Brief number--for 1975 Tech Briefs

    Assessment of registration methods for thermal infrared and visible images for diabetic foot monitoring

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    This work presents a revision of four different registration methods for thermal infrared and visible images captured by a camera-based prototype for the remote monitoring of diabetic foot. This prototype uses low cost and off-the-shelf available sensors in thermal infrared and visible spectra. Four different methods (Geometric Optical Translation, Homography, Iterative Closest Point, and Affine transform with Gradient Descent) have been implemented and analyzed for the registration of images obtained from both sensors. All four algorithms´ performances were evaluated using the Simultaneous Truth and Performance Level Estimation (STAPLE) together with several overlap benchmarks as the Dice coefficient and the Jaccard index. The performance of the four methods has been analyzed with the subject at a fixed focal plane and also in the vicinity of this plane. The four registration algorithms provide suitable results both at the focal plane as well as outside of it within 50 mm margin. The obtained Dice coefficients are greater than 0.950 in all scenarios, well within the margins required for the application at hand. A discussion of the obtained results under different distances is presented along with an evaluation of its robustness under changing conditions.This research was funded by the IACTEC Technological Training program, grant number TF INNOVA 2016–2021

    Integration Of Multispectral Camera Systems For Enhanced Visualization Biological Studies Using UAS

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    The purpose of this research is to enhance visualization of warm-blooded animals and analyze the vegetation in which they are located using a camera system mounted on Unmanned Aircraft System (UAS). The results are coherently displayed in a single image at the same spatial location so that biologists will have accurate animal counts along with vegetation conditions. Application of aerial imagery has been used to analyze the vegetation health and determining the number of the animals by the wildlife service. However, a major obstacle of the research is to combine the two imaging systems to obtain the same spatial image with enhanced visualization. The two camera systems used in this research are the Tetracam ADC lite multispectral and the FLIR Photon 320 infrared camera. These two camera systems each have a different lens, field of view and sensor array size. The project involves the alignment of the two cameras to pixel level for the spectral image analysis. The spectral image analysis provides both vegetation information, such as Normalized Difference Vegetation Index (NDVI), along with enhanced visualization of warm-blooded targets. The system was miniaturized for the standalone payload for aerospace applications including UAS. The FLIR Photon 320 was used to capture the infrared image and a Tetracam ADC lite multispectral camera was used to capture the near infrared, red and green spectral band images. A laboratory experimental setup was designed to mechanically align the two camera systems to get close identical spatial imagery. Spatial registration of the two images was performed using reverse image warping method by finding affine transformation matrix using point correspondences. Both camera systems were calibrated using Camera Calibration Toolbox for Matlab to reduce any distortion due to the lenses. A single board computer is used to capture and store the image data from FLIR Photon 320 infrared camera while the Tetracam image data is stored internally on board. The image capture time was set by continuous timed delay triggering within the Tetracam camera. The single board computer follows the Tetracam signals and matches the FLIR Photon 320 still image acquisition time with the Tetracam ADC lite. Once the images are captured and stored by the camera systems, the files are downloaded and image processing is conducted. The data was analyzed to calculate the NDVI to observe the plant health. The infrared spectral band was used to identify the warm-blooded animals. In addition, various false color combinations of spectral bands and normalized difference ratios are processed to observe the visual enhancement capabilities on the vegetation and warm-blooded animal. It was determined that detected warm-blooded animal in the infrared band registered on top of NDVI image to show the vegetation health in a single image produced effective results. The combined image data from the FLIR Photon 320 and Tetracam ADC lite produced meaningful vegetation and animal information in the single image. This enhanced the capacity to identify and count animals while simultaneously characterize the vegetation environment, which is highly desired in ecosystem studie

    Multispectral photography for earth resources

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    A guide for producing accurate multispectral results for earth resource applications is presented along with theoretical and analytical concepts of color and multispectral photography. Topics discussed include: capabilities and limitations of color and color infrared films; image color measurements; methods of relating ground phenomena to film density and color measurement; sensitometry; considerations in the selection of multispectral cameras and components; and mission planning
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