Mejoras en la Selección de Parámetros de un Método de Crecimiento de Regiones Mediante Semillas para la Segmentación de Imágenes Multibanda (Improving Parameters Selection of a Seeded Region Growing Method for Multiband Image Segmentation)

Javier Sanchez Hernandez (jarsahe@hotmail.com), Estibaliz Martínez Izquierdo (emartinez@fi.upm.es), Agueda Arquero Hidalgo (aarquero@fi.upm.es)


Universidad Politécnica de Madrid
This paper appears in: Revista IEEE América Latina

Publication Date: March 2015
Volume: 13,   Issue: 3 
ISSN: 1548-0992


Abstract:
In the last decade, Object Based Image Analysis (OBIA) has been accepted as an effective method for processing high spatial resolution multiband images. This image analysis method is an approach that starts with the segmentation of the image. Image segmentation in general is a procedure to partition an image into homogenous groups (segments). In practice, visual interpretation is often used to assess the quality of segmentation and the analysis relies on the experience of an analyst. In an effort to address the issue, in this study, we evaluate several seed selection strategies for an automatic image segmentation methodology based on a seeded region growing-merging approach. In order to evaluate the segmentation quality, segments were subjected to spatial autocorrelation analysis using Moran's I index and intra-segment variance analysis. We apply the algorithm to image segmentation using an aerial multiband image.

Index Terms:
image segmentation, seed selection, region growing, segmentation objective evaluation   


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