IMPROVED EDGE DETECTION FOR SATELLITE IMAGES

Abstract. Edges are a key feature employed in various computer vision applications namely segmentation, object recognition, feature tracking and 3D reconstruction. Edges provide key information with regards to object presence, shape, form and detail which aid in many computer vision tasks. While there are various edge detection techniques in literature, challenges in edge detection remain. Varying image contrast due to non uniform scene illumination and imaging resolution affects the edge information obtained from any given image. The edge detection results are characterised by missing edges, edge fragmentation and some false positive edges. Gradient based edge detectors are the most commonly used detectors. These detectors all suffer from aforementioned challenges. In this, paper we present an edge detection framework that aims to recover long unfragmented edges from satellite images. This is achieved by using an edge accumulator that operates on the entire edge detection parameter space. Gradient based edge detectors rely on thresholding to retrieve salient edges. This usually results in missed or noisy edges. To counter this, the accumulator is run over a wide parameter space, growing edges at each accumulator level while maintaining edge position using a localisation filter. The results are longer unbroken edges that are detected for most objects, even in shadowy regions and low contrast areas. The results show improved edge detection that preserves the form and detail of objects when compared to current gradient based detectors.

Angaben zum Objekt

• Standort: Deutsche Nationalbibliothek Frankfurt am Main
• Umfang: Online-Ressource
• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: IMPROVED EDGE DETECTION FOR SATELLITE IMAGES ; volume:V-2-2022 ; year:2022 ; pages:185-192 ; extent:8 ; ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; V-2-2022 (2022), 185-192 (gesamt 8)

Beteiligte, Orts- und Zeitangaben

• Urheber: Mapurisa, W. ; Sithole, G.

Weitere Informationen

• DOI: 10.5194/isprs-annals-V-2-2022-185-2022
• URN: urn:nbn:de:101:1-2022051905321649413468
• Rechteinformation: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Letzte Aktualisierung: 15.08.2025, 07:39 MESZ