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Shimoni M.,Signal and Image Center | Haelterman R.,Royal Military Academy | Perneel C.,Royal Military Academy
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2013

Thermal hyperspectral imaging (THI) extends the activities of automatic spectral based target detection to the thermal infrared spectral range. This imaging technique which allows the detection of targets regardless of illumination, unfortunately, frequently suffers from high false alarm rates due to the high noise contained in the image. This study develops a new Normalised Difference Thermal Index NDTIMM which allows the physics -based segmentation of Man-Made objects in the scene. This index produces quick and efficient separation of man-made objects containing silicate minerals from other natural and man-made materials. The change detectors are spatially adapted to segments which present different NDTIMM ratios. As a result, the scene is enhanced and the performances of change detection methods which are based on NDTIMM segments distance are significantly improved. © 2013 IEEE.


Shimoni M.,Signal and Image Center | Tolt G.,Swedish Defence Research Agency | Perneel C.,Royal Military Academy | Ahlberg J.,Swedish Defence Research Agency
Workshop on Hyperspectral Image and Signal Processing, Evolution in Remote Sensing | Year: 2011

This paper presents a new method to automatically detect occluded vehicle in semi or deep shadow areas using combined very high resolution (VHR) 3D LIDAR and hyperspectral data. The proposed shape/spectral integration (SSI) decision fusion algorithm was shown to outperform the spectral based anomaly algorithm mainly in deep shadow areas. The fusion of LIDAR DSM data with spectral data is useful in the detection of vehicles in semi and deep shadow areas. The utility of shape information was shown to be a way to enhance spectral target detection in complex urban scene. © 2011 IEEE.


Shimoni M.,Signal and Image Center | Perneel C.,Royal Military Academy
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2012

The results present in this paper, show the improvement of 15%-17% in the accuracy for most of the classes while applying SEM-S method. © 2012 IEEE.


Moser G.,University of Genoa | Tuia D.,University of Zurich | Shimoni M.,Signal and Image Center
IEEE Geoscience and Remote Sensing Magazine | Year: 2015

The 2015 Data Fusion Contest, organized by the Image Analysis and Data Fusion Technical Committee (IADF TC) of the IEEE Geoscience and Remote Sensing Society (GRSS), aims at providing a challenging image analysis opportunity, including multiresolution and multisensor fusion at extremely high resolution. The 2015 Contest involves two datasets acquired simultaneously by passive and active sensors. The passive data is a 5cm-resolution color ortho-photo acquired in the visible wavelength range. The active data source is a 65 pts/m2 LiDAR 3D point cloud that is provided both as raw 3D coordinates (ASCII format) and as a Digital Surface Model (DSM) with a point spacing of about 10 cm. The laser scan rate, angle, and frequency were 125 Hz, 20°, and 49 Hz, respectively. The LiDAR data include the first, last and one intermediate return. © 2013 IEEE.


Shimoni M.,Signal and Image Center | Tolt G.,Swedish Defence Research Agency | Perneel C.,Royal Military Academy | Ahlberg J.,Swedish Defence Research Agency
International Geoscience and Remote Sensing Symposium (IGARSS) | Year: 2011

In an effort to overcome the limitations of small target detection in complex urban scene, complementary data sets are combined to provide additional insight about a particular scene. This paper presents a method based on shape/spectral integration (SSI) decision level fusion algorithm to improve the detection of vehicles in semi and deep shadow areas. A four steps process combines high resolution LIDAR and hyperspectral data to classify shadow areas, segment vehicles in LIDAR data, detect spectral anomalies and improves vehicle detection. The SSI decision level fusion algorithm was shown to outperform detection using a single data set and the utility of shape information was shown to be a way to enhance spectral target detection in complex urban scenes. © 2011 IEEE.

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