EARTHQUAKE-DAMAGED REGIONS DETECTION FROM HIGH RESOLUTION IMAGE BASED ON SUPER-PIXEL SEGMENTATION AND DEEP LEARNING Article Swipe
C. Liu
,
Haigang Sui
,
Peng Yan
,
Hua Li
,
Qinghua Li
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.5194/isprs-annals-v-3-2020-45-2020
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.5194/isprs-annals-v-3-2020-45-2020
Accurate detection and automatic processing of earthquake-damaged regions is essential for effective rescue and post-disaster reconstruction. In this study, we proposed a Combined Super-pixel Segmentation and AlexNet Detection approach (CSSAD) for automatically extracting damaged regions from post-earthquake high-resolution images. Simple Linear Iterative Clustering (SLIC) algorithm was used to segment the high resolution images to obtain more homogeneous geo-objects. Multiscale samples database, which took the different scale effect of damaged regions into account, was constructed based on the geometric centre of each super-pixel. AlexNet, which achieved the automatic extraction of high-level features and accurate identification of target geo-objects, was used to detect the damaged regions. To enhance the localization accuracy, the output of AlexNet was further refined using super-pixel segmentations and masked out of shadow and vegetation. Compared with traditional method, the proposed approach effectively reduces the false and missed detection ratio at least 10 percent.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/isprs-annals-v-3-2020-45-2020
- https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2020/45/2020/isprs-annals-V-3-2020-45-2020.pdf
- OA Status
- diamond
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3047472853
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3047472853Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/isprs-annals-v-3-2020-45-2020Digital Object Identifier
- Title
-
EARTHQUAKE-DAMAGED REGIONS DETECTION FROM HIGH RESOLUTION IMAGE BASED ON SUPER-PIXEL SEGMENTATION AND DEEP LEARNINGWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-08-03Full publication date if available
- Authors
-
C. Liu, Haigang Sui, Peng Yan, Hua Li, Qinghua LiList of authors in order
- Landing page
-
https://doi.org/10.5194/isprs-annals-v-3-2020-45-2020Publisher landing page
- PDF URL
-
https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2020/45/2020/isprs-annals-V-3-2020-45-2020.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-3-2020/45/2020/isprs-annals-V-3-2020-45-2020.pdfDirect OA link when available
- Concepts
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Computer science, Artificial intelligence, Pixel, Segmentation, Shadow (psychology), Pattern recognition (psychology), Computer vision, Cluster analysis, High resolution, Image segmentation, Remote sensing, Geology, Psychology, PsychotherapistTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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27Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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