 |
Technical Program
Paper Detail
| Paper: | TA-L1.6 |
| Session: | Image Segmentation |
| Time: | Tuesday, October 10, 11:40 - 12:00 |
| Presentation: |
Lecture
|
| Title: |
HIERARCHICAL MRF-BASED SEGMENTATION OF REMOTE-SENSING IMAGES |
| Authors: |
Raffaele Gaetano; Università di Napoli Federico II | | | | | Giovanni Poggi; Università di Napoli Federico II | | | | | Giuseppe Scarpa; Università di Napoli Federico II | | |
| Abstract: |
Remote-sensing images are often composed by a hierarchy of nested regions, with complex regions that are regarded as homogeneous at some observation scale, but can be further segmented at finer scales. Tree-structured Markov random fields (TS-MRF) allow one to model such images, and to develop efficient segmentation algorithms for them. TS-MRF are traditionally based on binary trees of classes, but the use of generic trees, with more degrees of freedom, can likely provide a better performance, as was shown in [1] with reference to synthetic images. Here we build upon the ideas proposed in [1] to devise a segmentation algorithm that works effectively, and with a limited computational burden, on real-world remote sensing images. |
|
