Actas de congresos
AUTOMATICALLY DETECTING TEXTUAL CONTENT IN HIGH-RESOLUTION IMAGES
Fecha
2021-01-01Registro en:
International Geoscience and Remote Sensing Symposium (IGARSS), v. 2021-July, p. 4204-4207.
10.1109/IGARSS47720.2021.9553189
2-s2.0-85126017224
Autor
Universidade Estadual Paulista (UNESP)
Natural Resources Institute
Institución
Resumen
Classifying targets in satellite images is a nontrivial task which requires dealing with a large number of undesirable elements such as clouds, building shadows and other unexpected objects. Among these, a commonly found element refers to artificially inserted post-processing objects like textual content, as the added text usually takes the form of watermarks, sensor specifications, street and place location names, etc. Manually selecting text segments is tedious, time-consuming, and requires the familiarity with image editing tools to precisely delineate these writing areas. Therefore, in this paper, a new automatic approach for detecting textual elements in satellite images is presented. Our approach combines cartoon-texture decomposition, thresholding-based rules, morphological operations, and connected component analysis into a fully automated and concise framework. Experiments on real satellite images and comparisons against well-established text detection methods demonstrate the high accuracy and low false-positive rate achieved by our approach when detecting textual content.