The PASEO Research Group

Research Goals & Objectives

The main goal is to develop new methods of image analysis to answer specific needs of planetary and space sciences specialists, e.g. the hyperspectral and/or multimodal image analysis in astronomy and Earth sciences. However, the increasing wealth and complexity of space imaging data is a challenge for image processing.

Data understanding • Accurate inference • Efficient analysis tools

The Aladin Sky Atlas (CDS) is a Virtual Observatory interface, integrating many visualisation and processing tools.

• The data are modeled within a probabilistic framework, thus enabling us to integrate multiple sources and modalities, possibly multi- or hyperspectral depending on the sensor type. [more details]
• Bayesian inference is used to estimate the quantities of interest as well as their uncertainties, thus allowing for accurate data analysis, model checking and assessment. [more details]
• We seek to develop efficient algorithms and fast implementations, in contact with specialists of each area. The new tools are to be integrated into a Virtual Observatory framework (e.g. via a web interface) through collaborations with the CDS and other research institutions or industrial partners (e.g. IAP, IAS, OCA).

Research Projects: Application areas / Data type

Our research projects are summarized in the following table. Projects are displayed according to the target application domain (columns) and source image type to be processed (rows). [click buttons for details]

APPLICATION		Astronomy, Astrophysics	Planetary Imaging (planets, small bodies)	Remote Sensing (Earth Observation)
DATA TYPE
image sources	spectral depth
single image	mono band	PNSpectrum	SurfaceModelRender SplitSplatRender	MTFInferFractal DispMapInference BayesCameraCalibra...
multiple images			3DShapeInference
single image	multi band	QTreeMultiSegment MissingMultiSegmentA FuzzyMarkovSegment QTreeRestoFusion CopulaHMTSegment LSBGalaxyDetect GalaxyClassification		MissingMultiSegmentE CorrNoiseWaterSegm... ImPolar
multiple images		DeepSkyFusion	3DSpaceFusion	ReflectanceFusion
single image	hyper spectral	HyperGalaxyClass MultiColorViz AstroCubeSegmentat... CubeDeconvolution CubeSourceSeparation
multiple images		CubeCombinaisonFus... CubeVisualization

An Interdisciplinary Research

Our application areas range from deep sky exploration to Earth observation, aiming at a better understanding of space imaging in order to provide new tools for the specialists of these areas.

• Astronomy (star fields, galaxies, nebulae) → image enhancement and improved visualisation (e.g. denoising, color coding), data fusion for an easier data manipulation (e.g. channel fusion, multi-source fusion, data reduction), automatic analysis and physics-based interpretation (e.g. segmentation, classification).
• Planetary Imaging (Solar System planets, satellites, small bodies) → image enhancement (e.g. deblurring, PSF estimation), model-based data fusion (e.g. 3D surface reconstruction, super-resolution), reflectance estimation and analysis (e.g. terrain classification).
• Remote Sensing (Earth observations at various resolutions) → image enhancement (e.g. blind deconvolution), model-based data fusion (e.g. topography reconstruction, super-resolution, data reduction), bidirectional reflectance estimation and analysis (e.g. segmentation).

Analyzing Various Data Types & Modalities

Depending on the application, various types of input images are used and the processing methods must adapt consequently. We separate the methods into two 3 classes based on the spectral depth (one, several, many bands). For each class we have two subclasses, whether a single or multiple images are required.

• Image sources: (single image vs. multiple images) → One input image is used in classical techniques (e.g. deblurring, segmentation). However some tasks require at least two images (e.g. surface recovery), other require even more (e.g. bidirectional reflectance inference). In some cases the task itself is dictated by the great number of available sources (e.g. data fusion). Multiple modalities, other than image-like data, can also be accounted for (e.g. existing elevation models, radar measurements).
• Spectral depth: (single band vs. multi or hyperspectral) → Single band images are the simplest to handle, whereas multispectral images require to account for band interactions and increased dimensionality of the problem (Hughes phenomenon). In addition, hyperspectral data usually require dimensionality reduction before further processing.