IMHE OpenIR  > Journal of Mountain Science  > Journal of Mountain Science-2015  > Vol12 No.3
Segmentation of High Spatial Resolution Remote Sensing Images of Mountainous Areas Based on the Improved Mean Shift Algorithm
LU Heng; LIU Chao; LI Nai-wen; GUO Jia-wei
Corresponding AuthorLIU Chao
2015-05
Source PublicationJournal of Mountain Science
ISSN1672-6316
Volume12Issue:3Pages:671-681
Subtype期刊论文
AbstractUsing conventional Mean Shift Algorithm to segment high spatial resolution Remote sensing images of mountainous areas usually leads to an unsatisfactory result, due to its rich texture information. In this paper, we propose an improved Mean Shift Algorithm in consideration of the characteristics of these images. First, images were classified into several homogeneous color regions and texture regions by conducting variance detection on the color space. Next, each homogeneous color region was directly segmented to generate the preliminary results by applying the Mean Shift Algorithm. For each texture region, we conduct a high-dimensional feature space by extracting information such as color, texture and shape comprehensively, and work out a proper bandwidth according to the normalized distribution density. Then the bandwidth variable Mean Shift Algorithm was applied to obtain segmentation results by conducting the patternclassification in feature space. Last, the final results were obtained by merging these regions by means of the constructed cost functions and removing the over-segmented regions from the merged regions. It has been experimentally segmented on the high spatial resolution remote sensing images collected by Quickbird and Unmanned Aerial Vehicle (UAV). We put forward an approach to evaluatethe segmentation results by using the segmentation matching index(SMI). This takes into consideration both the area and the spectrum. The experimental results suggest that the improved Mean Shift Algorithm outperforms the conventional one in terms of accuracy of segmentation.
KeywordMean Shift Image Segmentation Region Merging Uav Image Quickbird Image
DOI10.1007/s11629-014-3332-6
Indexed BySCI
Language英语
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/9101
CollectionJournal of Mountain Science_Journal of Mountain Science-2015_Vol12 No.3
Corresponding AuthorLIU Chao
Recommended Citation
GB/T 7714
LU Heng,LIU Chao,LI Nai-wen,et al. Segmentation of High Spatial Resolution Remote Sensing Images of Mountainous Areas Based on the Improved Mean Shift Algorithm[J]. Journal of Mountain Science,2015,12(3):671-681.
APA LU Heng,LIU Chao,LI Nai-wen,&GUO Jia-wei.(2015).Segmentation of High Spatial Resolution Remote Sensing Images of Mountainous Areas Based on the Improved Mean Shift Algorithm.Journal of Mountain Science,12(3),671-681.
MLA LU Heng,et al."Segmentation of High Spatial Resolution Remote Sensing Images of Mountainous Areas Based on the Improved Mean Shift Algorithm".Journal of Mountain Science 12.3(2015):671-681.
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