TILTON , James

A new spectral-spatial method for classification of hyperspectral images is proposed. The HSegClas method is based on the integration of probabilistic classification and shape analysis within the hierarchical step-wise optimization algorithm. First, probabilistic support vector machines classification is applied. Then, at each iteration two...

International audience

The melting of sea ice is correlated to increases in sea surface temperature and associated climatic changes. Therefore, it is important to investigate how rapidly sea ice floes melt. For this purpose, a new TempoSeg method for multitemporal segmentation of multiyear ice floes is proposed. The microwave radiometer is used to track the position...

The Hierarchical SEGmentation (HSEG) algorithm, which combines region object finding with region object clustering, has given good performances for multi- and hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary....

Best merge region growing normally produces seg- mentations with closed connected region objects. Recognizing that spectrally similar objects often appear in spatially separate loca- tions, we present an approach for tightly integrating best merge region growing with nonadjacent region object aggregation, which we call hierarchical segmentation...

Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used...