Dr. Niclas Wadströmer Profile

Dr. Niclas Wadströmer

Senior scientist at Swedish Defence Research Agency

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Publications (7)

Proceedings Article | 21 October 2016 Paper

Deep subspace mapping in hyperspectral imaging

Niclas Wadströmer, David Gustafsson, Henrik Perersson, David Bergström

Proceedings Volume 9988, 99880Q (2016) https://doi.org/10.1117/12.2241771

KEYWORDS: Hyperspectral imaging, Associative arrays, Pattern recognition, Visualization, Sensors, Computer programming, Principal component analysis, Data modeling, Neural networks, Neurons

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Proceedings Article | 11 October 2010 Paper

Co-aligning aerial hyperspectral push-broom strips for change detection

Erik Ringaby, Jörgen Ahlberg, Niclas Wadströmer, Per-Erik Forssén

Proceedings Volume 7835, 78350Y (2010) https://doi.org/10.1117/12.865034

KEYWORDS: Sensors, Gyroscopes, Image sensors, Global Positioning System, Imaging systems, Image registration, Image restoration, Electro optical sensors, Scanners, Mechanical sensors

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Proceedings Article | 13 May 2010 Paper

A new hyperspectral dataset and some challenges

Niclas Wadströmer, Jörgen Ahlberg, Thomas Svensson

Proceedings Volume 7695, 76950M (2010) https://doi.org/10.1117/12.851325

KEYWORDS: Sensors, Stochastic processes, Data modeling, Visualization, Near infrared, Global Positioning System, Visible radiation, Spectral resolution, Infrared signatures, Edge roughness

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Proceedings Article | 13 May 2010 Paper

An information measure of sensor performance and its relation to the ROC curve

Jörgen Ahlberg, Ingmar Renhorn, Niclas Wadströmer

Proceedings Volume 7695, 769520 (2010) https://doi.org/10.1117/12.851322

KEYWORDS: Sensors, Sensor performance, Target detection, Detection and tracking algorithms, Quantization, Berkelium, Algorithm development, Hyperspectral imaging, Atmospheric optics, Defense and security

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Proceedings Article | 29 April 2010 Paper

MOMS: a multi-optical sensor system for detection of surface laid mines

Dietmar Letalick, Ingmar Renhorn, Ove Steinvall, Niclas Wadströmer

Proceedings Volume 7664, 76640I (2010) https://doi.org/10.1117/12.850041

KEYWORDS: Sensors, Land mines, Mining, LIDAR, Improvised explosive devices, Spatial resolution, Vegetation, Signal processing, Reflectivity, Target detection

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Proceedings Article | 28 September 2009 Paper

Compression of hyperspectral data for automated analysis

Anna Linderhed, Niclas Wadströmer, K.-G. Stenborg, Harald Nautsch

Proceedings Volume 7477, 74770P (2009) https://doi.org/10.1117/12.830343

KEYWORDS: Image compression, Sensors, Quantization, Video compression, Video, Hyperspectral imaging, Data compression, Standards development, Target detection, Data modeling

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State of the art and coming hyperspectral optical sensors generate large amounts of data and automatic analysis is necessary. One example is Automatic Target Recognition (ATR), frequently used in military applications and a coming technique for civilian surveillance applications. When sensors communicate in networks, the capacity of the communication channel defines the limit of data transferred without compression. Automated analysis may have different demands on data quality than a human observer, and thus standard compression methods may not be optimal. This paper presents results from testing how the performance of detection methods are affected by compressing input data with COTS coders. A standard video coder has been used to compress hyperspectral data. A video is a sequence of still images, a hybrid video coder use the correlation in time by doing block based motion compensated prediction between images. In principle only the differences are transmitted. This method of coding can be used on hyperspectral data if we consider one of the three dimensions as the time axis. Spectral anomaly detection is used as detection method on mine data. This method finds every pixel in the image that is abnormal, an anomaly compared to the surroundings. The purpose of anomaly detection is to identify objects (samples, pixels) that differ significantly from the background, without any a priori explicit knowledge about the signature of the sought-after targets. Thus the role of the anomaly detector is to identify "hot spots" on which subsequent analysis can be performed. We have used data from Imspec, a hyperspectral sensor. The hyperspectral image, or the spectral cube, consists of consecutive frames of spatial-spectral images. Each pixel contains a spectrum with 240 measure points. Hyperspectral sensor data was coded with hybrid coding using a variant of MPEG2. Only I- and P- frames was used. Every 10th frame was coded as I frame. 14 hyperspectral images was coded in 3 different directions using x, y, or z direction as time. 4 different quantization steps were used. Coding was done with and without initial quantization of data to 8 bbp. Results are presented from applying spectral anomaly detection on the coded data set.

Proceedings Article | 18 April 2008 Paper

An information theoretic model of target discrimination using hyperspectral and multisensor data

Niclas Wadströmer, Ingmar Renhorn

Proceedings Volume 6940, 69402H (2008) https://doi.org/10.1117/12.780192

KEYWORDS: Sensors, Data modeling, Electro optical modeling, Stochastic processes, Statistical modeling, Information theory, Target detection, Environmental sensing, Electro optical sensors, Detection and tracking algorithms

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Showing 5 of 7 publications

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