IDENTIFICATION OF NUCLEAR ACTIVITIES USING SATELLITE IMAGING
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1 S07 - NEW TRENDS IN COMMERCIAL SATELLITE IMAGERY IDENTIFICATION OF NUCLEAR ACTIVITIES USING SATELLITE IMAGING S. Baude, C. Guérin, J.M. Lagrange, R. Marion, B. Puysségur and D. Schwartz
2 CONTEXT AND TOPICS Satellite imagery is a very performing tool for the monitoring of human and industrial activity. Due to the multiplicity of modalities, its scope is very broad and it participates actively in the monitoring of nuclear activities. Visible 3D change detection urban scene dredged material Radar imaging change detection image quality, target recognition Improved characterization using Hyperspectral imaging materials gaz
3 VISIBLE IMAGING : DSM HANDLING Approach : New Digital Surface Model processing [Guérin] Multi-stereoscopic images Occlusion handling Results : Increased accuracy of building edges Better homogeneity Great effect of multi-stereoscopy (reconstruction from more than 2 stereoscopic views) [Guérin] C. Guérin, R. Binet and M. Pierrot-Deseilligny. Automatic detection of elevation changes by differential DSM analysis: application to urban areas. IEEE Journal of Selected Topics in Applied earth observation and Remote Sensing, 2014.
4 VISIBLE IMAGING : DSM HANDLING DSM FROM 2 STEREOSCOPIC VIEWS DSM FROM 3 STEREOSCOPIC VIEWS
5 VISIBLE IMAGING : 3D CHANGE DETECTION BUILDING CHANGE DETECTION MAPS FROM 2 DSM Approach : Dealing with occlusion areas Taking into account DSM correlation scores
6 VISIBLE IMAGING : 3D CHANGE DETECTION BUILDING CHANGE DETECTION MAPS FROM 2 DSM Approach : Dealing with occlusion areas Taking into account DSM correlation scores Positive change (building destruction) Negative change (backfilled material)
7 VISIBLE IMAGING : 3D CHANGE DETECTION BUILDING CHANGE DETECTION MAPS FROM 2 DSM Ground truth validation
8 RADAR IMAGING Interest for non proliferation studies Radar : any time mode night an day independant of weather conditions Improved analysis by increasing image quality Examination of anthropic activity : change detection Detection of elements of interest and recognition in addition to optical analysis Very high potential of interferometric images analysis
9 Approach : RADAR: IMAGE QUALITY IMPROVEMENT SUPER-IMAGES Time series analysis : interferometric image acquisition radar/radar registration if necessary [Tupin] spatial averaging (geometric mean) Result : signal to scatter enhancement improved analysis of structured elements Application : fusion of 7 or 16 images [Tupin] Florence Tupin, Henri Maître, Jean-François Mangin, Jean-Marie Nicolas, and Eugène Pechersky. Detection of Linear Features in SAR Images: Application to Road Network Extraction. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 36, NO. 2, MARCH 1998.
10 RADAR: IMAGE QUALITY IMPROVEMENT SUPER-IMAGES RADAR AMPLITUDE SUPER-IMAGE TSX ILLUSTRATION OF A 7 SET FUSION
11 Approach [Quin] : Time series analysis : interferometric image acquisition Algorithm in three steps : Result: RADAR: CHANGE DETECTION 1. analysis of the amplitude distribution of two Hölder means statistical model estimation 2. the probability density function localizes the changes as being out of the image statistic. 3. automatic thresholding based on the false alarm rate Simple and fast detection of changes from radar images A single parameter Application : automatic vehicules detection from 6 images [Quin] G. Quin, B. Pinel-Puysségur, J.M. Nicolas, P. Loreaux. MIMOSA: an automatic change detection method for SAR time series. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2013.
12 RADAR: CHANGE DETECTION Change between Image 1 and 2 Vehicule appears Vehicule disappears ILLUSTRATION OF RADAR CHANGE DETECTION SUPERIMPOSED ON OPTICAL IMAGE (Google)
13 RADAR: CHANGE DETECTION Change between Image 2 and 3 Vehicule appears Vehicule disappears ILLUSTRATION OF RADAR CHANGE DETECTION SUPERIMPOSED ON OPTICAL IMAGE (Google)
14 RADAR: CHANGE DETECTION Change between Image 3 and 4 Vehicule appears Vehicule disappears ILLUSTRATION OF RADAR CHANGE DETECTION SUPERIMPOSED ON OPTICAL IMAGE (Google)
15 RADAR: CHANGE DETECTION Change between Image 4 and 5 Vehicule appears Vehicule disappears ILLUSTRATION OF RADAR CHANGE DETECTION SUPERIMPOSED ON OPTICAL IMAGE (Google)
16 RADAR: CHANGE DETECTION Change between Image 5 and 6 Vehicule appears Vehicule disappears ILLUSTRATION OF RADAR CHANGE DETECTION SUPERIMPOSED ON OPTICAL IMAGE (Google)
17 RADAR: TARGET DETECTION AND RECOGNITION RADAR AMPLITUDE SUPER-IMAGE TSX
18 RADAR: TARGET DETECTION AND RECOGNITION RADAR AMPLITUDE SUPER-IMAGE TSX
19 HYPERSPECTRAL IMAGING In complement to optical imaging, hyperspectral imaging provides additionnal informations due to rich spectral content atmosphere chlorophyll Atmosphere attenuation bands wavelength (nm) Spectral resolution 10 nm Spectral range ~ µm ~ 200 spectral bands Hyperspectral cube
20 HYPERSPECTRAL IMAGING : MATERIAL CHARACTERIZATION Results of optical images analysis Site n 1 Site n 2 From optical images, the 2 sites reveal : brown / red laggons, white heap no information on industrial process
21 HYPERSPECTRAL IMAGING : MATERIAL CHARACTERIZATION Results of hyperspectral images analysis Site n 1 Bauxaline heap Site n 2 (résidue) White gypsum (product) Limestone (treatment) Red gypsum (résidue) Bauxite heap (ore) Alumina heap (product) From hyperspectral images : Site 1 : confirmation of alumina elaboration Site 2 : confirmation of presence of H 2 SO 4 in TiO 2 process
22 HYPERSPECTRAL IMAGING : GAZ CHARACTERIZATION (application to CO2) No gaz release visible CO2 plume visible on hyperspectral image Optical image CO2 transmittance wavelength (µm) Hyperspectral image K-means segmentation CO2 identification (Clustred Tunes Match Filter)
23 FURTHER WORKS Visible imaging Uncertainties estimation of DSM and 3D change detection Radar imaging Relaxing interferometric conditions Hyperspectral imaging Performing semi-automatic spectrum identification Spectrum library building in progress
24 BACKUP SLIDE hyperspectral analysis Site n 1 Bauxaline heap (résidue) Al-OH Fer Bauxite heap (ore) wavelength (nm) Alumina heap (product) Bauxaline absorptions in visible Alumina spectrum Absorption in IR PAGE 24
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