AN UNSUPERVISED SEGMENTATION METHOD FOR REMOTE SENSING IMAGERY BASED ON CONDITIONAL RANDOM FIELDS

Autor(es)

Soares, A. R. ; Korting, T. S. ; Fonseca, L. M. G. ; Neves, A. K. ; IEEE

• Tipo de documento: Artigo Científico
• Fonte: 2020 IEEE LATIN AMERICAN GRSS & ISPRS REMOTE SENSING CONFERENCE (LAGIRS); v. N/A, p. 5-pg., 2020-01-01.

Resumo

Segmentation is a fundamental problem in imageprocessing and a common operation in Remote Sensing, which has been widely used especially in Geographic Object-Based mage Analysis (GEOBIA). In this paper, we propose a new unsupervised segmentation algorithm based on the Conditional Random Fields (CRF) theory, The method relies on two levels of information: (1) that comes from an unsupervised classification with Fuzzy C-Means algorithm; (2) the 8-connected neighbourhood of a pixel. The algorithm was tested on a WorldView-2 multispectral image, with 2m of spatial resolution, Results were evaluated using 6 quality measures, and their performance was compared with other image segmentation algorithms that are usually applied by the Remote Sensing community. Results indicate that the proposed algorithm achieved superior overall performance when compared others, despite some over-segmentation. (AU)

• Processo FAPESP: 17/24086-2 - Gerenciamento de metadados de grandes volumes de dados de sensoriamento remoto
• Beneficiário: Thales Sehn Körting
• Modalidade de apoio: Auxílio à Pesquisa - Regular