Special Lecture - 4/17, 11:00AM, Duncan Hall 1049

Jocelyn Chanussot

Engineering School on Energy, Water and Environment (ENSE3)
Grenoble Institute of Technology (Grenoble INP)

"Hierarchical Analysis of Hyperspectral Images Using Binary Partition Trees"

After decades of use of multispectral remote sensing, most of the major space agencies now have new programs to launch hyperspectral sensors, recording the reflectance information of each point on the ground in hundreds of narrow and contiguous spectral bands. The spectral information is instrumental for the accurate analysis of the physical component present in one scene. But, every rose has its thorns: most of the traditional signal and image processing algorithms fail when confronted to such high dimensional data (each pixel is represented by a vector with several hundreds of dimensions).

In this talk, we focus on the extension to hyperspectral data of a very powerful image processing analysis tool: the Binary Partition Tree (BPT). It provides a generic hierarchical representation of images and consists of the two following steps:

• construction of the tree: one starts from the pixel level and merge pixels/regions progressively until the top of the hierarchy (the whole image is considered as one single region) is reached. To proceed, one needs to define a model to represent the regions (for instance: the average spectrum - but this is not a good idea) and one also needs to define a similarity measure between neighboring regions to decide which ones should be merged first (for instance the euclidean distance between the model of each region - but this is not a good idea either). This step (construction of the tree) is very much related to the data.
• the second step is the pruning of the tree: this is very much related to the considered application. The pruning of the tree leads to one segmentation. The resulting segmentation might not be any of the result obtained during the iterative construction of the tree. This is where this representation outperforms the standard approaches. But one may also perform classification, or objet detection (assuming an object of interest will appear somewhere as one noode of the tree, the game is to define a suitable criterion, related to the application, to find this node).

Results are presented on various hyperspectral images.