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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.
    Aidantausta, Oskar
    et al.
    Linköping University, Department of Computer and Information Science.
    Asman, Patrick
    Linköping University, Department of Computer and Information Science.
    Land Use/Land Cover Classification From Satellite Remote Sensing Images Over Urban Areas in Sweden: An Investigative Multiclass, Multimodal and Spectral Transformation, Deep Learning Semantic Image Segmentation Study2023Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    Remote Sensing (RS) technology provides valuable information about Earth by enabling an overview of the planet from above, making it a much-needed resource for many applications. Given the abundance of RS data and continued urbanisation, there is a need for efficient approaches to leverage RS data and its unique characteristics for the assessment and management of urban areas. Consequently, employing Deep Learning (DL) for RS applications has attracted much attention over the past few years. In this thesis, novel datasets consisting of satellite RS images over urban areas in Sweden were compiled from Sentinel-2 multispectral, Sentinel-1 Synthetic Aperture Radar (SAR) and Urban Atlas 2018 Land Use/Land Cover (LULC) data. Then, DL was applied for multiband and multiclass semantic image segmentation of LULC. The contributions of complementary spectral, temporal and SAR data and spectral indices to LULC classification performance compared to using only Sentinel-2 data with red, green and blue spectral bands were investigated by implementing DL models based on the fully convolutional network-based architecture, U-Net, and performing data fusion. Promising results were achieved with 25 possible LULC classes. Furthermore, almost all DL models at an overall model level and all DL models at an individual class level for most LULC classes benefited from complementary satellite RS data with varying degrees of classification improvement. Additionally, practical knowledge and insights were gained from evaluating the results and are presented regarding satellite RS data characteristics and semantic segmentation of LULC in urban areas. The obtained results are helpful for practitioners and researchers applying or intending to apply DL for semantic segmentation of LULC in general and specifically in Swedish urban environments.

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    LULC_classification_satellite_remote_sensing
  • 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 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.

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  • 5.
    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.

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  • 6.
    Domènech-Gil, Guillem
    et al.
    Linköping University, Department of Thematic Studies, Tema Environmental Change. Linköping University, Faculty of Arts and Sciences.
    Nguyen, Thanh Duc
    Linköping University, Department of Thematic Studies, Tema Environmental Change. Linköping University, Faculty of Science & Engineering.
    Wikner, J. Jakob
    GE Healthcare, Linköping, Sweden.
    Eriksson, Jens
    Linköping University, Department of Physics, Chemistry and Biology, Sensor and Actuator Systems. Linköping University, Faculty of Science & Engineering.
    Puglisi, Donatella
    Linköping University, Department of Physics, Chemistry and Biology, Sensor and Actuator Systems. Linköping University, Faculty of Science & Engineering.
    Bastviken, David
    Linköping University, Department of Thematic Studies, Tema Environmental Change. Linköping University, Faculty of Arts and Sciences.
    Efficient Methane Monitoring with Low-Cost Chemical Sensorsand Machine Learning2024Conference paper (Refereed)
    Abstract [en]

    We present a method to monitor methane at atmospheric concentrations with errors inthe order of tens of parts per billion. We use machine learning techniques and periodic calibrationswith reference equipment to quantify methane from the readings of an electronic nose. The resultsobtained demonstrate versatile and robust solution that outputs adequate concentrations in a varietyof different cases studied, including indoor and outdoor environments with emissions arising fromnatural or anthropogenic sources. Our strategy opens the path to a wide-spread use of low-costsensor system networks for greenhouse gas monitoring.

  • 7.
    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.

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  • 8.
    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

  • 9.
    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.

  • 10.
    Karlson, Martin
    Linköping University, Department of Thematic Studies, Tema Environmental Change. Linköping University, Faculty of Arts and Sciences. Linköping University, Centre for Climate Science and Policy Research, CSPR.
    Multi-source Mapping of Peatland Types using Sentinel-1, Sentinel-2 and Terrain Derivatives – A Comparison Between Five High-latitude Landscapes2023Data set
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    dataset
  • 11.
    Karlson, Martin
    Linköping University, Department of Thematic Studies, Tema Environmental Change. Linköping University, Faculty of Arts and Sciences. Linköping University, Centre for Climate Science and Policy Research, CSPR.
    Multi-source mapping of peatland types using Sentinel-1, Sentinel-2 and terrain derivatives – A comparison between five high-latitude landscapes: Remote sensing predictor variables and field reference data2022Data set
    Abstract [en]

    Dataset used in the publication "Multi-source mapping of peatland types using Sentinel-1, Sentinel-2 and terrain derivatives – A comparison between five high-latitude landscapes". The dataset includes preprocessed predictor variables in image format (geoTIFF) from Sentinel-1, Sentinel-2 and Copernicus DEM for the five sites, including North Slope (Alaska), Yukon (Canada), Great Slave Lake (Canada), Hudson Bay Lowlands (Canada) and northern Sweden (Scandinavia). It also includes reference data (shape files) used for training and validation of classification models.

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    Yukon_S1_VH
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    Yukon_S1_VV
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    Toolik_S1_VH
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    Toolik_VV
  • 12.
    Karlson, Martin
    et al.
    Linköping University, Department of Thematic Studies, Tema Environmental Change. Linköping University, Faculty of Arts and Sciences.
    Bastviken, David
    Linköping University, Department of Thematic Studies, Tema Environmental Change. Linköping University, Faculty of Arts and Sciences.
    Multi‐Source Mapping of Peatland Types Using Sentinel‐1, Sentinel‐2, and Terrain Derivatives—A Comparison Between Five High‐Latitude Landscapes2023In: Journal of Geophysical Research - Biogeosciences, ISSN 2169-8953, E-ISSN 2169-8961, Vol. 128, no 4, article id e2022JG007195Article in journal (Refereed)
    Abstract [en]

    Mapping wetland types in northern-latitude regions with Earth Observation (EO) data is important for several practical and scientific applications, but at the same time challenging due to the variability and dynamic nature in wetland features introduced by differences in geophysical conditions. The objective of this study was to better understand the ability of Sentinel-1 radar data, Sentinel-2 optical data and terrain derivatives derived from Copernicus digital elevation model to distinguish three main peatland types, two upland classes, and surface water, in five contrasting landscapes located in the northern parts of Alaska, Canada and Scandinavia. The study also investigated the potential benefits for classification accuracy of using regional classification models constructed from region-specific training data compared to a global classification model based on pooled reference data from all five sites. Overall, the results show high promise for classifying peatland types and the three other land cover classes using the fusion approach that combined all three EO data sources (Sentinel-1, Sentinel-2 and terrain derivatives). Overall accuracy for the individual sites ranged between 79.7% and 90.3%. Class specific accuracies for the peatland types were also high overall but differed between the five sites as well as between the three classes bog, fen and swamp. A key finding is that regional classification models consistently outperformed the global classification model by producing significantly higher classification accuracies for all five sites. This suggests for progress in identifying effective approaches for continental scale peatland mapping to improve scaling of for example, hydrological- and greenhouse gas-related processes in Earth system models.

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  • 13.
    Karlson, Martin
    et al.
    Linköping University, Department of Thematic Studies, Tema Environmental Change. Linköping University, Faculty of Arts and Sciences. Linköping University, Centre for Climate Science and Policy Research, CSPR.
    Bastviken, David
    Linköping University, Department of Thematic Studies, Tema Environmental Change. Linköping University, Faculty of Arts and Sciences.
    Reese, Heather
    Univ Gothenburg, Sweden.
    Error Characteristics of Pan-Arctic Digital Elevation Models and Elevation Derivatives in Northern Sweden2021In: Remote Sensing, E-ISSN 2072-4292, Vol. 13, no 22, article id 4653Article in journal (Refereed)
    Abstract [en]

    Many biochemical processes and dynamics are strongly controlled by terrain topography, making digital elevation models (DEM) a fundamental dataset for a range of applications. This study investigates the quality of four pan-Arctic DEMs (Arctic DEM, ASTER DEM, ALOS DEM and Copernicus DEM) within the Kalix River watershed in northern Sweden, with the aim of informing users about the quality when comparing these DEMs. The quality assessment focuses on both the vertical accuracy of the DEMs and their abilities to model two fundamental elevation derivatives, including topographic wetness index (TWI) and landform classification. Our results show that the vertical accuracy is relatively high for Arctic DEM, ALOS and Copernicus and in our study area was slightly better than those reported in official validation results. Vertical errors are mainly caused by tree cover characteristics and terrain slope. On the other hand, the high vertical accuracy does not translate directly into high quality elevation derivatives, such as TWI and landform classes, as shown by the large errors in TWI and landform classification for all four candidate DEMs. Copernicus produced elevation derivatives with results most similar to those from the reference DEM, but the errors are still relatively high, with large underestimation of TWI in land cover classes with a high likelihood of being wet. Overall, the Copernicus DEM produced the most accurate elevation derivatives, followed by slightly lower accuracies from Arctic DEM and ALOS, and the least accurate being ASTER.

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  • 14.
    Lunn, Simon
    Linköping University, Department of Thematic Studies.
    CLASSIFYING DOMINANT PARKLAND SPECIES IN A WEST AFRICAN AGROFORESTRY LANDSCAPE USING PLEIADES SATELLITE IMAGERY2020Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    As we move towards a digital based society, technology continues to improve. It is important to take advantage of this to inform and facilitate our sustainable development goals in the most cost-effective and time efficient manner. By utilising the best available technologies, not only can time savings be achieved, but scope of works can be dramatically increased, particularly with ecological data collection. This study will focus on collecting ecological data (tree species) using developing modern technologies (satellites) with the aim of reaching classification accuracies comparable with ground truthed (real life) records. The study area is in central Burkina Faso approximately 30km south of the capital and is generally described as an agroforestry parklands area. The region suffers greatly from poverty and many people are heavily dependent on the agricultural sector and subsistence farming. As these agroforestry parklands are so critical to many people’s livelihoods, it is important to assess the natural resources available within them to provide the best food security management for the people.

    Tree species locations were overlayed on two satellite images acquired during different stages of the annual growing periods in the agroforestry parklands of the study area. From these images, segmentation of individual tree crowns was done manually and used as the reference data for an object-based classification model, which were assessed for the classification accuracies that can be achieved. Three satellite image scenarios were assessed for classification accuracy, including two single image scenarios and a multi-imagery dataset combining both images.

    Results indicate that combined images perform the best in terms of overall classification accuracies, closely followed by the end of the wet season growing period. The image acquisition from the end of the dry season was quite poor in comparison, having an overall classification accuracy more than 10% lower than the other scenarios.

    Of the focus species assessed in this study, Azadirachta Indica was the clear loser in terms of the number of correctly classified individuals from each model scenario. All other focus species were relatively well classified achieving close to or above 60% accuracies in the multi-imagery classification scenario.

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  • 15.
    Sher, Rabnawaz Jan
    Linköping University, Department of Computer and Information Science, The Division of Statistics and Machine Learning.
    Classification of a Sensor Signal Attained By Exposure to a Complex Gas Mixture2021Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
    Abstract [en]

    This thesis is carried out in collaboration with a private company, DANSiC AB This study is an extension of a research work started by DANSiC AB in 2019 to classify a source. This study is about classifying a source into two classes with the sensitivity of one source higher than the other as one source has greater importance. The data provided for this thesis is based on sensor measurements on different temperature cycles. The data is high-dimensional and is expected to have a drift in measurements. Principal component analysis (PCA) is used for dimensionality reduction. “Differential”, “Relative” and “Fractional” drift compensation techniques are used for compensating the drift in data. A comparative study was performed using three different classification algorithms, which are “Linear Discriminant Analysis (LDA)”, “Naive Bayes classifier (NB)” and “Random forest (RF)”. The highest accuracy achieved is 59%,Random forest is observed to perform better than the other classifiers.

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  • 16.
    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. 

  • 17.
    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, 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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