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  • 1.
    Ahlberg, Jörgen
    Swedish Defence Research Agency (FOI), Linköping, Sweden.
    Estimating atmosphere parameters in hyperspectral data2010In: Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI / [ed] Sylvia S. Shen, Paul E. Lewis, SPIE - International Society for Optical Engineering, 2010, p. Art.nr. 7695-82-Conference paper (Refereed)
    Abstract [en]

    We address the problem of estimating atmosphere parameters (temperature, water vapour content) from data captured by an airborne thermal hyperspectral imager, and propose a method based on direct optimization. The method also involves the estimation of object parameters (temperature and emissivity) under the restriction that the emissivity is constant for all wavelengths. Certain sensor parameters can be estimated as well in the same process. The method is analyzed with respect to sensitivity to noise and number of spectral bands. Simulations with synthetic signatures are performed to validate the analysis, showing that estimation can be performed with as few as 10-20 spectral bands at moderate noise levels. More than 20 bands does not improvethe estimates. The proposedmethod is alsoextended to incorporateadditionalknowledge,for examplemeasurements ofatmospheric parameters and sensor noise.

  • 2.
    Ahlberg, Jörgen
    et al.
    Department of IR Systems, Division of Sensor Technology, Swedish Defence Research Agency (FOI), Linköping, Sweden.
    Renhorn, Ingmar
    Department of IR Systems, Division of Sensor Technology, Swedish Defence Research Agency (FOI), Linköping, Sweden.
    An information-theoretic approach to band selection2005In: Proc. SPIE 5811, Targets and Backgrounds XI: Characterization and Representation / [ed] Wendell R. Watkins; Dieter Clement; William R. Reynolds, SPIE - International Society for Optical Engineering, 2005, p. 15-23Conference paper (Refereed)
    Abstract [en]

    When we digitize data from a hyperspectral imager, we do so in three dimensions; the radiometric dimension, the spectral dimension, and the spatial dimension(s). The output can be regarded as a random variable taking values from a discrete alphabet, thus allowing simple estimation of the variable’s entropy, i.e., its information content. By modeling the target/background state as a binary random variable and the corresponding measured spectra as a function thereof, wecan compute theinformation capacity ofa certainsensoror sensor configuration. This can be used as a measure of the separability of the two classes, and also gives a bound on the sensor’s performance. Changing the parameters of the digitizing process, bascially how many bits and bands to spend, will affect the information capacity, and we can thus try to find parameters where as few bits/bands as possible gives us as good class separability as possible. The parameters to be optimized in this way (and with respect to the chosen target and background) are spatial, radiometric and spectral resolution, i.e., which spectral bands to use and how to quantize them. In this paper, we focus on the band selection problem, describe an initial approach, and show early results of target/background separation.

  • 3.
    Berg, Amanda
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Termisk Systemteknik AB, Linköping, Sweden.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, The Institute of Technology. Termisk Systemteknik AB, Linköping, Sweden.
    Classification and temporal analysis of district heating leakages in thermal images2014In: Proceedings of The 14th International Symposium on District Heating and Cooling, 2014Conference paper (Other academic)
    Abstract [en]

    District heating pipes are known to degenerate with time and in some cities the pipes have been used for several decades. Due to bad insulation or cracks, energy or media leakages might appear. This paper presents a complete system for large-scale monitoring of district heating networks, including methods for detection, classification and temporal characterization of (potential) leakages. The system analyses thermal infrared images acquired by an aircraft-mounted camera, detecting the areas for which the pixel intensity is higher than normal. Unfortunately, the system also finds many false detections, i.e., warm areas that are not caused by media or energy leakages. Thus, in order to reduce the number of false detections we describe a machine learning method to classify the detections. The results, based on data from three district heating networks show that we can remove more than half of the false detections. Moreover, we also propose a method to characterize leakages over time, that is, repeating the image acquisition one or a few years later and indicate areas that suffer from an increased energy loss.

  • 4.
    Berg, Amanda
    et al.
    Linköping University, Department of Electrical Engineering, Computer Vision. Linköping University, The Institute of Technology. Termisk Systemteknik AB, Linköping, Sweden.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, The Institute of Technology. Termisk Systemteknik AB, Linköping, Sweden.
    Classification of leakage detections acquired by airborne thermography of district heating networks2014In: 2014 8th IAPR Workshop on Pattern Recognition in Remote Sensing (PRRS), IEEE , 2014, p. 1-4Conference paper (Refereed)
    Abstract [en]

    We address the problem of reducing the number offalse alarms among automatically detected leakages in districtheating networks. The leakages are detected in images capturedby an airborne thermal camera, and each detection correspondsto an image region with abnormally high temperature. Thisapproach yields a significant number of false positives, and wepropose to reduce this number in two steps. First, we use abuilding segmentation scheme in order to remove detectionson buildings. Second, we extract features from the detectionsand use a Random forest classifier on the remaining detections.We provide extensive experimental analysis on real-world data,showing that this post-processing step significantly improves theusefulness of the system.

  • 5.
    Friman, Ola
    et al.
    Linköping University, Department of Biomedical Engineering, Medical Informatics. Linköping University, The Institute of Technology. Swedish Defence Research Agency, Linköping, Sweden.
    Follo, Peter
    Swedish Defence Research Agency, Linköping, Sweden.
    Ahlberg, Jörgen
    Linköping University, Department of Electrical Engineering, Information Coding. Linköping University, The Institute of Technology. Termisk Systemteknik AB, Linköping, Sweden.
    Sjökvist, Stefan
    Termisk Systemteknik AB, Linköping, Sweden.
    Methods for Large-Scale Monitoring of District Heating Systems Using Airborne Thermography2014In: IEEE Transactions on Geoscience and Remote Sensing, ISSN 0196-2892, E-ISSN 1558-0644, Vol. 52, no 8, p. 5175-5182Article in journal (Refereed)
    Abstract [en]

    District heating is a common way of providing heat to buildings in urban areas. The heat is carried by hot water or steam and distributed in a network of pipes from a central powerplant. It is of great interest to minimize energy losses due to bad pipe insulation or leakages in such district heating networks. As the pipes generally are placed underground, it may be difficult to establish the presence and location of losses and leakages. Toward this end, this work presents methods for large-scale monitoring and detection of leakages by means of remote sensing using thermal cameras, so-called airborne thermography. The methods rely on the fact that underground losses in district heating systems lead to increased surface temperatures. The main contribution of this work is methods for automatic analysis of aerial thermal images to localize leaking district heating pipes. Results and experiences from large-scale leakage detection in several cities in Sweden and Norway are presented.

  • 6.
    Friman, Ola
    et al.
    Swedish Defence Research Agency, Linköping, Sweden.
    Tolt, Gustav
    Swedish Defence Research Agency, Linköping, Sweden.
    Ahlberg, Jörgen
    Termisk Systemteknik, Linköping, Sweden.
    Illumination and shadow compensation of hyperspectral images using a digital surface model and non-linear least squares estimation2011In: Proc. SPIE 8180, Image and Signal Processing for Remote Sensing XVII / [ed] Lorenzo Bruzzone, SPIE - International Society for Optical Engineering, 2011, p. Art.nr 8180-26-Conference paper (Refereed)
    Abstract [en]

    Object detection and material classification are two central tasks in electro-optical remote sensing and hyperspectral imaging applications. These are challenging problems as the measured spectra in hyperspectral images from satellite or airborne platforms vary significantly depending on the light conditions at the imaged surface, e.g., shadow versus non-shadow. In this work, a Digital Surface Model (DSM) is used to estimate different components of the incident light. These light components are subsequently used to predict what a measured spectrum would look like under different light conditions. The derived method is evaluated using an urban hyperspectral data set with 24 bands in the wavelength range 381.9 nm to 1040.4 nm and a DSM created from LIDAR 3D data acquired simultaneously with the hyperspectral data

  • 7.
    Hamoir, Dominique
    et al.
    Onera – The French Aerospace Lab, Toulouse, France.
    Hespel, Laurent
    Onera – The French Aerospace Lab, Toulouse, France.
    Déliot, Philippe
    Onera – The French Aerospace Lab, Toulouse, France.
    Boucher, Yannick
    Onera – The French Aerospace Lab, Toulouse, France.
    Steinvall, Ove
    Swedish Defense Research Agency (FOI), Linköping, Sweden.
    Ahlberg, Jörgen
    Swedish Defense Research Agency (FOI), Linköping, Sweden.
    Larsson, Håkan
    Swedish Defense Research Agency (FOI), Linköping, Sweden.
    Letalick, Dietmar
    Swedish Defense Research Agency (FOI), Linköping, Sweden.
    Lutzmann, Peter
    Fraunhofer-IOSB, Ettlingen, Germany.
    Repasi, Endre
    Fraunhofer-IOSB, Ettlingen, Germany.
    Ritt, Gunnar
    Fraunhofer-IOSB, Ettlingen, Germany.
    Results of ACTIM: an EDA study on spectral laser imaging2011In: Proc. SPIE 8186, Electro-Optical Remote Sensing, Photonic Technologies, and Applications V / [ed] Gary W. Kamerman; Ove Steinvall; Gary J. Bishop; John D. Gonglewski; Keith L. Lewis; Richard C. Hollins; Thomas J. Merlet, SPIE - International Society for Optical Engineering, 2011, p. Art.nr 8186A-25-Conference paper (Refereed)
    Abstract [en]

    The European Defence Agency (EDA) launched the Active Imaging (ACTIM) study to investigate the potential of active imaging, especially that of spectral laser imaging. The work included a literature survey, the identification of promising military applications, system analyses, a roadmap and recommendations.   Passive multi- and hyper-spectral imaging allows discriminating between materials. But the measured radiance in the sensor is difficult to relate to spectral reflectance due to the dependence on e.g. solar angle, clouds, shadows... In turn, active spectral imaging offers a complete control of the illumination, thus eliminating these effects. In addition it allows observing details at long ranges, seeing through degraded atmospheric conditions, penetrating obscurants (foliage, camouflage…) or retrieving polarization information. When 3D, it is suited to producing numerical terrain models and to performing geometry-based identification. Hence fusing the knowledge of ladar and passive spectral imaging will result in new capabilities.  We have identified three main application areas for active imaging, and for spectral active imaging in particular: (1) long range observation for identification, (2) mid-range mapping for reconnaissance, (3) shorter range perception for threat detection. We present the system analyses that have been performed for confirming the interests, limitations and requirements of spectral active imaging in these three prioritized applications.

  • 8.
    Wadströmer, Niclas
    et al.
    Swedish Defence Research Institute (FOI), Linköping, Sweden.
    Ahlberg, Jörgen
    Swedish Defence Research Institute (FOI), Linköping, Sweden.
    Svensson, Thomas
    Swedish Defence Research Institute (FOI), Linköping, Sweden.
    A new hyperspectral dataset and some challenges2010In: Proc. SPIE 7695, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVI / [ed] Sylvia S. Shen; Paul E. Lewis, SPIE - International Society for Optical Engineering, 2010, p. Art.nr. 7695-22-Conference paper (Refereed)
    Abstract [en]

    We present a new hyperspectral data set that FOI will keep publicly available. The hyperspectral data set was collected in an airborne measurement over the countryside. The spectral resolution was about 10 nm which allowed registrations in 60 spectral bands in the visual and near infrared range (390-960 nm). Objects with various signature properties were placed in three areas: the edge of a wood, an open field and a rough open terrain. Several overflights were performed over the areas. Between the overflights some of the objects were moved, representing different scenarios. Our interest is primarily in anomaly detection of man-made objects placed in nature where no such objects are expected. The objects in the trial were military and civilian vehicles, boards of different size and a camouflage net. The size of the boards range from multipixel to subpixel size. Due to wind and cloud conditions the stability and the flight height of the airplane vary between the overflights, which makes the analysis extra challenging. 

  • 9.
    Wang, Gaihua
    et al.
    Hubei Collaborative Innovation Centre for High-efficiency Utilization of Solar Energy, Hubei University of Technology, China / School of Electrical and Electronic Engineering, Hubei University of Technology, China.
    Liu, Yang
    School of Electrical and Electronic Engineering, Hubei University of Technology, China / Faculty of Technology, University of Vaasa, Vaasa, Finland.
    Xiong, Caiquan
    School of Computer Science, Hubei University of Technology, China.
    An Optimization Clustering Algorithm Based on Texture Feature Fusion for Color Image Segmentation2015In: Algorithms, ISSN 1999-4893, E-ISSN 1999-4893, Vol. 8, no 2, p. 234-247Article in journal (Refereed)
    Abstract [en]

    We introduce a multi-feature optimization clustering algorithm for color image segmentation. The local binary pattern, the mean of the min-max difference, and the color components are combined as feature vectors to describe the magnitude change of grey value and the contrastive information of neighbor pixels. In clustering stage, it gets the initial clustering center and avoids getting into local optimization by adding mutation operator of genetic algorithm to particle swarm optimization. Compared with well-known methods, the proposed method has an overall better segmentation performance and can segment image more accurately by evaluating the ratio of misclassification.

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