Automatic Generation of Seamline Network Using Area Voronoi Diagrams With Overlap

Pan, Jun; Wang, Mi; Li, D.; Li, J.

doi:10.1109/tgrs.2008.2009880

Cited by 69 publications

(21 citation statements)

References 14 publications

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“…A total of 14 images from Landsat 5 Thematic Mapper (TM) (eight images), Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (two images) and Landsat 8 Operational Land Imager (OLI) (four images) were acquired for the specified years. Seamless images were produced based on Voroni diagrams that locate the bisector between images; adjacent edges were identified as seamlines constraining effective mosaic polygons that specify inclusion pixels for the final mosaicked product, permitting less visible boundaries through blending overlapping pixels [36]. Mosaicked images were subsequently clipped to the original PMR study area boundary.…”

Section: Data Preprocesingmentioning

confidence: 99%

Urban Growth Dynamics in Perth, Western Australia: Using Applied Remote Sensing for Sustainable Future Planning

MacLachlan

Biggs

Roberts

et al. 2017

Land

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Earth observation data can provide valuable assessments for monitoring the spatial extent of (un)sustainable urban growth of the world's cities to better inform planning policy in reducing associated economic, social and environmental costs. Western Australia has witnessed rapid economic expansion since the turn of the century founded upon extensive natural resource extraction. Thus, Perth, the state capital of Western Australia, has encountered significant population and urban growth in response to the booming state economy. However, the recent economic slowdown resulted in the largest decrease in natural resource values that Western Australia has ever experienced. Here, we present multi-temporal urban expansion statistics from 1990 to 2015 for Perth, derived from Landsat imagery. Current urban estimates used for future development plans and progress monitoring of infill and density targets are based upon aggregated census data and metrics unrepresentative of actual land cover change, underestimating overall urban area. Earth observation provides a temporally consistent methodology, identifying areal urban area at higher spatial and temporal resolution than current estimates. Our results indicate that the spatial extent of the Perth Metropolitan Region has increased 45% between 1990 and 2015, over 320 km 2 . We highlight the applicability of earth observation data in accurately quantifying urban area for sustainable targeted planning practices.

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Section: Data Preprocesingmentioning

confidence: 99%

Urban Growth Dynamics in Perth, Western Australia: Using Applied Remote Sensing for Sustainable Future Planning

MacLachlan

Biggs

Roberts

et al. 2017

Land

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“…The two Landsat scenes covering the study area were combined to form a seamless image mosaic following the methodology of Pan et al (2009). Voroni diagrams were created on the bisector between images with adjacent edges defined as seamlines, identifying effective mosaic polygons that specify pixels from each image to include in the final mosaic, facilitating less visible boundaries through blending of overlapping pixels (Pan et al 2009) (Figure 1(a)).…”

Section: Landsat Preprocessingmentioning

confidence: 99%

“…Voroni diagrams were created on the bisector between images with adjacent edges defined as seamlines, identifying effective mosaic polygons that specify pixels from each image to include in the final mosaic, facilitating less visible boundaries through blending of overlapping pixels (Pan et al 2009) (Figure 1(a)). Due to remaining residual noise in the mosaicked imagery caused by factors such as the brightening effect of thin clouds and atmospheric correction differences, surface reflectance values were standardized following the approach identified by Sexton et al (2013):…”

Section: Landsat Preprocessingmentioning

confidence: 99%

Subpixel land-cover classification for improved urban area estimates using Landsat

MacLachlan

Roberts

Biggs

et al. 2017

International Journal of Remote Sensing

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Urban areas are Earth's fastest growing land use that impact hydrological and ecological systems and the surface energy balance. The identification and extraction of accurate spatial information relating to urban areas is essential for future sustainable city planning owing to its importance within global environmental change and human-environment interactions. However, monitoring urban expansion using medium resolution (30-250 m) imagery remains challenging due to the variety of surface materials that contribute to measured reflectance resulting in spectrally mixed pixels. This research integrates high spatial resolution orthophotos and Landsat imagery to identify differences across a range of diverse urban subsets within the rapidly expanding Perth Metropolitan Region (PMR), Western Australia. Results indicate that calibrating Landsat-derived subpixel land-cover estimates with correction values (calculated from spatially explicit comparisons of subpixel Landsat values to classified high-resolution data which accounts for over [under] estimations of Landsat) reduces moderate resolution urban area over (under) estimates by on an average 55.08% for the PMR. This approach can be applied to other urban areas globally through use of frequently available and/or low-cost high spatial resolution imagery (e.g. using Google Earth). This will improve urban growth estimations to help monitor and measure change whilst providing metrics to facilitate sustainable urban development targets within cities around the world. ARTICLE HISTORY

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“…In terms of the computational efficiency, the proposed method also has a high efficiency. Moreover, the proposed method can easily be applied to the seamlines network determination framework easily [3,46,47].…”

Section: Discussionmentioning

confidence: 99%

Automatic Seamline Determination for Urban Image Mosaicking Based on Road Probability Map from the D-LinkNet Neural Network

Yuan¹,

Yang

Li³

et al. 2020

Sensors

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Image mosaicking which is a process of constructing multiple orthoimages into a single seamless composite orthoimage, is one of the key steps for the production of large-scale digital orthophoto maps (DOM). Seamline determination is one of the most difficult technologies in the automatic mosaicking of orthoimages. The seamlines that follow the centerlines of roads where no significant differences exist are beneficial to improve the quality of image mosaicking. Based on this idea, this paper proposes a novel method of seamline determination based on road probability map from the D-LinkNet neural network for urban image mosaicking. This method optimizes the seamlines at both the semantic and pixel level as follows. First, the road probability map is obtained with the D-LinkNet neural network and related post processing. Second, the preferred road areas (PRAs) are determined by binarizing the road probability map of the overlapping area in the left and right image. The PRAs are the priority areas in which the seamlines cross. Finally, the final seamlines are determined by Dijkstra’s shortest path algorithm implemented with binary min-heap at the pixel level. The experimental results of three group data sets show the advantages of the proposed method. Compared with two previous methods, the seamlines obtained by the proposed method pass through the less obvious objects and mainly follow the roads. In terms of the computational efficiency, the proposed method also has a high efficiency.

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Automatic Generation of Seamline Network Using Area Voronoi Diagrams With Overlap

Cited by 69 publications

References 14 publications

Urban Growth Dynamics in Perth, Western Australia: Using Applied Remote Sensing for Sustainable Future Planning

Urban Growth Dynamics in Perth, Western Australia: Using Applied Remote Sensing for Sustainable Future Planning

Subpixel land-cover classification for improved urban area estimates using Landsat

Automatic Seamline Determination for Urban Image Mosaicking Based on Road Probability Map from the D-LinkNet Neural Network

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