9. High-level processing (astronomical data analysis)

Transcription

1 Master ISTI / PARI / IV Introduction to Astronomical Image Processing 9. High-level processing (astronomical data analysis) André Jalobeanu LSIIT / MIV / PASEO group Jan lsiit-miv.u-strasbg.fr/paseo PASEO

2 High-level processing: astronomical data analysis Supervised analysis Pixel classification/segmentation Search for particular elements or structures Object classification Star classification, Galaxy classification Physical parameter estimation Physical parameter field estimation Velocities, density, ionization, element abundances Object models PN shape and expansion rates, density models Data mining Star mapping & astrometric reduction Sky surveys, unknown object detection Unsupervised pixel classification/segmentation Unsupervised object classification

3 Supervised analysis Recall the principles of supervised data classification and image segmentation See some examples of object classification (galaxies, stars) Understand the differences between spatial and spectral classification Focus on labeling rather than physical interpretation

4 Supervised classification and segmentation N classes, known parameters or models (training) Feature space Data classification Random samples (no spatial organization) Data space feature space classes Hard classification: 1 label/element, binary Soft classification: N labels/element, binary Fuzzy classification: N membership fcts/element [0,1] Fuzzy Hard Image segmentation Decompose the image into connected regions (common properties: color, texture...) Classification with spatial consistency Probabilistic vs. variational approaches Common framework: optimization Segmentation boundaries (multiband nebula image)

5 Spectral element identification Hyperspectral images (IFS) Use spectral signatures of elements: catalog of known signatures (line positions) Process each spatial sample (spaxel) independently as a spectrum Multispectral images Transform known signatures (integration): look for particular colors (e.g. H red, O green, dust blue) Maximum or minimum finding (1D signal) Statistical fitting (regression): more accurate Non-orthogonal change of basis (projections)

6 Pixel classification/segmentation Training step Use catalog of spectral signatures Use expert knowledge to manually assign labels to test images Find optimal feature spaces Classification / segmentation Classify or segment, depending on spatial dependencies and regularization Astrobiology at ASU Training step for supervised image classification in rock composition analysis (X. Shi)

7 Object classification (morphological) Extract sources Use morphological characteristics to classify objects PSF-shaped objects: stars Bigger, elliptical or round objects: galaxies (different types) Elongated tracks: cosmic rays, asteroids, space debris Diffuse objects: gas nebulae or galaxies... Feature extraction (math. morphology and parameter estimation) Classifier training needed Examples of morphological indicators in galaxy classification: surface brightness and concentration index [Odewahn 95]

8 Object classification (spectral) Use spectral information Spatial information might be insufficient or difficult to use Use color indices extracted from bands or spectra (e.g. B-V, U-V, R-I where I=infrared, R=red, V=green&yellow, B=blue, U=ultraviolet) Predefined spectral types for stars Loose correspondence between morphological types and color indices in galaxy classification Star classes, spectral types and luminosity (HR diagram) measure L (photometry), measure T (spectrum fitting or color index X-Y for multispectral) assign a class Example of spectral indicator in galaxy classification: color (O-E bands) [Odewahn 95]

9 Physical parameter estimation Grasp the basic ideas of physical parameter measurement Focus on quantitative physical interpretation of astronomical data Discover the advantages of 3D object modeling

10 Physical parameter estimation from spectra shifted line position: object velocity line position: element ID e.g. Voigt profile continuum: Star surface temperature line intensity: gas density line profile: Gas pressure Statistical fitting (regression) line fitting, spectrum model fitting synchrotron radiation, etc.

11 Physical parameter fields spatial distribution from IFS or data cubes For each pixel (spectrum): estimate physical parameters independently Spatial consistency: Use spatial priors (e.g. smoothness) Constrain physical parameter estimation to stabilize the solution Regularization: variational, probabilistic (MRF) Optimization methods NGC 4254 velocity field using multiscale regularization via Hidden Markov Trees (M. Petremand) Metallicity index (NGC 3377) (P.T. de Zeeuw) Mean velocity (M32) Velocity field of NGC 4486 (F. Eisenhauer)

12 Physical parameter fields Kinematics and line strength distributions examples Galaxy formation observed with Sauron (M. Cappellari)

13 3D/2D object models 3D object modeling Physical models of astronomical objects (e.g. planetary nebulae) density, shape, expansion rate... Stronger constraints than 2D models some parameters can be correctly determined only using 3D models: estimation from both image and spectrum Parameter estimation Directly in 3D: difficult Multigrid optimization: nested 2D models to initialize 3D optimization Planetary Nebula parameter estimation, ambiguity removal using long-slit spectra (A.R. Hajian et al.)

14 Data mining See some modern approaches to source extraction & measurement Grasp some ideas about automated classification and segmentation Get acquainted to information discovery from large data sets

15 Astrometric reduction for star-like objects Measure star positions and magnitudes (build maps) First extract the stars Existing software, e.g. Sextractor Extraction algorithm: morphological cleaning & segmentation, or template matching with PSF, maxima finding (recursive: CLEAN) Measure the position Compute the centroid or fit an analytic profile Compute the magnitude Integrate over 2 circles (star and sky background) or fit analytic profile Compare with existing catalogs for identification

16 Sextractor : source extraction [Bertin 95] Automatic source extraction and analysis Very low level corrections Filtering and segmentation De-blending (spatial source separation) Photometry and astrometry, basic shape parameter computation Cross-identification using catalogs De-blending is necessary for spatially overlapping sources (multiple maxima for one region)

17 Sextractor shape parameters and layout Examples of shape parameters and moments computed for each detected source

18 Unsupervised pixel classification/segmentation Find class parameters & number of classes Learning process: the most difficult part! Sensitive to the number of bands (curse of dimensionality) Assign a label to each pixel Use spatial consistency priors (region-based segmentation) Multiscale segmentation of the SMC using Hidden Markov Trees and taking into account missing data (Flitti et al.)

19 Unsupervised object classification Example: AutoClass (Hanson-Stutz-Cheeseman, NASA Ames) Bayesian learning theory: automatic parameter estimation (e.g. class parameters) automatic model selection (e.g. number of classes) Probability theory & optimization Bayesian re-classification of the IRAS LRS Atlas [Goebel 89] Autoclass discovery in the IRAS star atlas (subtle differences btw. infrared spectra) 2 subgroups

20 Content-based image retrieval Process huge catalogs (>50M galaxies for SLOAN catalog) Goal: facilitate search operations Indexing is needed: feature extraction each object is located in a multidimensional feature space Avoid processing images for each query! ZCAT redshift catalog (distribution on the sky) Preprocessing for indexing: crop, center, PCA [Calleja et al.] Galaxy retrieval system [Calleja et al.]

21 Further reading Galaxies and the universe (course notes) Supervised classification (remote sensing) SAURON IFS - instrument and image examples Astrometric reduction from the IRIS tutorial Sextractor Astrostatistics Tutorials SAMSI Astrostatistics Course Notes SU Selected articles in statistics for astronomy & physics