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Open AccessEditor’s ChoiceArticle

Operational Large-Scale Segmentation of Imagery Based on Iterative Elimination

Landcare Research, Private Bag 11052, Palmerston North 4442, New Zealand
Department of Geography and Earth Sciences, Aberystwyth University, Aberystwyth SY23 3DB, UK
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(6), 658;
Received: 28 February 2019 / Accepted: 15 March 2019 / Published: 18 March 2019
(This article belongs to the Special Issue Image Segmentation for Environmental Monitoring)
Image classification and interpretation are greatly aided through the use of image segmentation. Within the field of environmental remote sensing, image segmentation aims to identify regions of unique or dominant ground cover from their attributes such as spectral signature, texture and context. However, many approaches are not scalable for national mapping programmes due to limits in the size of images that can be processed. Therefore, we present a scalable segmentation algorithm, which is seeded using k-means and provides support for a minimum mapping unit through an innovative iterative elimination process. The algorithm has also been demonstrated for the segmentation of time series datasets capturing both the intra-image variation and change regions. The quality of the segmentation results was assessed by comparison with reference segments along with statistics on the inter- and intra-segment spectral variation. The technique is computationally scalable and is being actively used within the national land cover mapping programme for New Zealand. Additionally, 30-m continental mosaics of Landsat and ALOS-PALSAR have been segmented for Australia in support of national forest height and cover mapping. The algorithm has also been made freely available within the open source Remote Sensing and GIS software Library (RSGISLib).
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Keywords: image segmentation; remote sensing; land cover; iterative elimination; RSGISLib image segmentation; remote sensing; land cover; iterative elimination; RSGISLib
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MDPI and ACS Style

Shepherd, J.D.; Bunting, P.; Dymond, J.R. Operational Large-Scale Segmentation of Imagery Based on Iterative Elimination. Remote Sens. 2019, 11, 658.

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