@article {aCalderero12, title = {Multispectral Cooperative Partition Sequence Fusion for Joint Classification and Hierarchical Segmentation}, journal = {Geoscience and Remote Sensing Letters, IEEE}, volume = {9}, year = {2012}, pages = {1012-1016}, abstract = {

In this letter, a region-based fusion methodology is presented for joint classification and hierarchical segmentation of specific ground cover classes from high-spatial-resolution remote sensing images. Multispectral information is fused at the partition level using nonlinear techniques, which allows the different relevance of the various bands to be fully exploited. A hierarchical segmentation is performed for each individual band, and the ensuing segmentation results are fused in an iterative and cooperative way. At each iteration, a consensus partition is obtained based on information theory and is combined with a specific ground cover classification. Here, the proposed approach is applied to the extraction and segmentation of vegetation areas. The result is a hierarchy of partitions with the most relevant information of the vegetation areas at different levels of resolution. This system has been tested for vegetation analysis in high-spatial-resolution images from the QuickBird and GeoEye satellites.

}, keywords = {GeoEye satellite, geophysical image processing, geophysical techniques, ground cover classification, hierarchical segmentation, high-spatial-resolution remote sensing images, image classification, image fusion, image region analysis, Image segmentation, information fusion, information theory, joint classification, Joints, Merging, multispectral cooperative partition sequence fusion, multispectral images, multispectral information, nonlinear techniques, partition level, QuickBird satellite, region merging, region-based fusion methodology, Remote sensing, Spatial resolution, specific ground cover classes, Vegetation mapping}, issn = {1545-598X}, doi = {10.1109/LGRS.2012.2188776}, author = {Calderero, F. and F. Eugenio and Marcello, J. and Marqu{\'e}s, F.} } @article {aMarcello08, title = {Motion estimation techniques to automatically track oceanographic thermal structures in multi-sensor image sequences}, journal = {IEEE transactions on geoscience and remote sensing}, volume = {46}, number = {9}, year = {2008}, pages = {2743{\textendash}2762}, issn = {0196-2892}, author = {Marcello, J. and F. Eugenio and Marqu{\'e}s, F. and Hernandez-Guerra, A. and Gasull, A.} } @conference {cEugenio03, title = {Marine coastal dynamic study using an automatic structure detection and spatial registration tool}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS-03)}, year = {2003}, pages = {1{\textendash}3}, isbn = {0-7803-7930-6}, author = {F. Eugenio and Marcello, J. and Marqu{\'e}s, F.} } @conference {cGiro-i-Nieto02, title = {MPEG-7 Descriptors for Earth Observation Satellites}, booktitle = {International Astronautical Congress}, year = {2002}, month = {09/2002}, pages = {1{\textendash}4}, publisher = {Inernational Astronautical Federation}, organization = {Inernational Astronautical Federation}, address = {Houston, Texas (USA)}, abstract = {

The amount of digital multimedia information has experienced a spectacular growth during the last years thanks to the advances on digital systems of image, video and audio acquisition. As a response to the need of organizing all this information, ISO/IEC has developed a new standard for multimedia content description called MPEG-7. Among other topics, MPEG-7 defines a set of multimedia descriptors that can be automatically generated using signal processing techniques. Earth Observation Satellites generate large quantities of images stored on enormous databases that can take advantage of the new standard. An automatic indexation of these images using MPEG-7 meta-data can improve their contents management as well as simplify interaction between independent databases. This paper gives an overall description on MPEG-7 standard focusing on the low-level Visual Descriptors. These descriptors can be grouped into four categories: color, texture, shape and motion. Visual Color Descriptors represent the color distribution of an image in terms of a specified color space. Visual Texture Descriptors define the visual pattern of an image according to its homogeneities and non-homogeneities. Visual Shape Descriptors describe the shape of 2D and 3D objects being, at the same time, invariant to scaling, rotation and translation. Motion Descriptors give the essential characteristics of objects and camera motions.

These descriptors can be used individually or in combination to index and retrieve satellite images of the Earth from a database. For example, oceans and glaciers can be discerned based on their Color Descriptors, also cities and desert based on the Texture Descriptors, island images can be grouped using the Shape descriptors and cyclone trajectories studied and compared using Motion Descriptors.

}, author = {Xavier Gir{\'o}-i-Nieto and Marqu{\'e}s, F. and Marcello, J. and F. Eugenio} }