SAR imagery segmentation by statistical region growing and hierarchical merging |
| |
Authors: | EA Carvalho DM Ushizima FNS Medeiros CIO Martins RCP Marques INS Oliveira |
| |
Affiliation: | 1. Departamento de Teleinformática (DETI), Grupo de Processamento de Imagens (GPI), Universidade Federal do Ceará (UFC), Brazil;2. Math and Visualization Groups, Lawrence Berkeley National Laboratory, USA;3. Departamento de Engenharia Elétrica, Universidade Federal do Ceará, Campus de Sobral, Brazil |
| |
Abstract: | This paper presents an algorithm to segment synthetic aperture radar (SAR) images, corrupted by speckle noise. Most standard segmentation techniques may require speckle filtering previously. Our approach performs radar image segmentation using the original noisy pixels as input data, i.e. without any preprocessing step. The algorithm includes a statistical region growing procedure combined with hierarchical region merging. The region growing step oversegments the input radar image, thus enabling region aggregation by employing a combination of the Kolmogorov–Smirnov (KS) test with a hierarchical stepwise optimization (HSWO) algorithm for performance improvement. We have tested and assessed the proposed technique on artificially speckled image and real SAR data. |
| |
Keywords: | |
本文献已被 ScienceDirect 等数据库收录! |
|