期刊
REMOTE SENSING
卷 11, 期 6, 页码 -出版社
MDPI
DOI: 10.3390/rs11060658
关键词
image segmentation; remote sensing; land cover; iterative elimination; RSGISLib
类别
资金
- Ministry of Business Innovation & Employment (New Zealand) [C09X1709]
- New Zealand Ministry of Business, Innovation & Employment (MBIE) [C09X1709] Funding Source: New Zealand Ministry of Business, Innovation & Employment (MBIE)
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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