Exploration of Multitemporal COSMO-SkyMed Data via Interactive Tree-Structured MRF Segmentation

We propose a new approach for remote sensing data exploration, based on a tight human-machine interaction. The analyst uses a number of powerful and user-friendly image classification/segmentation tools to obtain a satisfactory thematic map, based only on visual assessment and expertise. All process...

Full description

Saved in:
Bibliographic Details
Published in:IEEE journal of selected topics in applied earth observations and remote sensing 2014-07, Vol.7 (7), p.2763-2775
Main Authors: Gaetano, Raffaele, Amitrano, Donato, Masi, Giuseppe, Poggi, Giovanni, Ruello, Giuseppe, Verdoliva, Luisa, Scarpa, Giuseppe
Format: Article
Language:English
Subjects:
Adaptation models
Agriculture
Data models
Image segmentation
Optical imaging
Remote sensing
Synthetic aperture radar
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We propose a new approach for remote sensing data exploration, based on a tight human-machine interaction. The analyst uses a number of powerful and user-friendly image classification/segmentation tools to obtain a satisfactory thematic map, based only on visual assessment and expertise. All processing tools are in the framework of the tree-structured MRF model, which allows for a flexible and spatially adaptive description of the data. We test the proposed approach for the exploration of multitemporal COSMO-SkyMed data, that we appropriately registered, calibrated, and filtered, obtaining a performance that is largely superior, in both subjective and objective terms, to that of comparable noninteractive methods.
ISSN:1939-1404
2151-1535
DOI:10.1109/JSTARS.2014.2316595