File Download
There are no files associated with this item.
Links for fulltext
(May Require Subscription)
- Publisher Website: 10.3390/rs10101596
- Scopus: eid_2-s2.0-85055438788
- WOS: WOS:000448555800096
Supplementary
- Citations:
- Appears in Collections:
Article: Delineation of built-up areas from very high-resolution satellite imagery using multi-scale textures and spatial dependence
Title | Delineation of built-up areas from very high-resolution satellite imagery using multi-scale textures and spatial dependence |
---|---|
Authors | |
Keywords | Built-up area detection Cross-scale High-resolution Multi-resolution wavelet Spatial dependence |
Issue Date | 2018 |
Citation | Remote Sensing, 2018, v. 10, n. 10, article no. 1596 How to Cite? |
Abstract | Very high spatial resolution (VHR) satellite images possess several advantages in terms of describing the details of ground targets. Extracting built-up areas from VHR images has received increasing attention in practical applications, such as land use planning, urbanization monitoring, geographic information database update. In this study, a novel method is proposed for built-up area detection and delineation on VHR satellite images, using multi-resolution space-frequency analysis, spatial dependence modelling and cross-scale feature fusion. First, the image is decomposed by multi-resolution wavelet transformation, and then the high-frequency information at different levels is employed to represent the multi-scale texture and structural characteristics of built-up areas. Subsequently, the local Getis-Ord statistic is introduced to model the spatial patterns of built-up area textures and structures by measuring the spatial dependence among frequency responses at different spatial positions. Finally, the saliency map of built-up areas is produced using a cross-scale feature fusion algorithm, followed by adaptive threshold segmentation to obtain the detection results. The experiments on ZY-3 and Quickbird datasets demonstrate the effectiveness and superiority of the proposed method through comparisons with existing algorithms. |
Persistent Identifier | http://hdl.handle.net/10722/329529 |
ISI Accession Number ID |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chen, Yixiang | - |
dc.contributor.author | Lv, Zhiyong | - |
dc.contributor.author | Huang, Bo | - |
dc.contributor.author | Jia, Yan | - |
dc.date.accessioned | 2023-08-09T03:33:27Z | - |
dc.date.available | 2023-08-09T03:33:27Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Remote Sensing, 2018, v. 10, n. 10, article no. 1596 | - |
dc.identifier.uri | http://hdl.handle.net/10722/329529 | - |
dc.description.abstract | Very high spatial resolution (VHR) satellite images possess several advantages in terms of describing the details of ground targets. Extracting built-up areas from VHR images has received increasing attention in practical applications, such as land use planning, urbanization monitoring, geographic information database update. In this study, a novel method is proposed for built-up area detection and delineation on VHR satellite images, using multi-resolution space-frequency analysis, spatial dependence modelling and cross-scale feature fusion. First, the image is decomposed by multi-resolution wavelet transformation, and then the high-frequency information at different levels is employed to represent the multi-scale texture and structural characteristics of built-up areas. Subsequently, the local Getis-Ord statistic is introduced to model the spatial patterns of built-up area textures and structures by measuring the spatial dependence among frequency responses at different spatial positions. Finally, the saliency map of built-up areas is produced using a cross-scale feature fusion algorithm, followed by adaptive threshold segmentation to obtain the detection results. The experiments on ZY-3 and Quickbird datasets demonstrate the effectiveness and superiority of the proposed method through comparisons with existing algorithms. | - |
dc.language | eng | - |
dc.relation.ispartof | Remote Sensing | - |
dc.subject | Built-up area detection | - |
dc.subject | Cross-scale | - |
dc.subject | High-resolution | - |
dc.subject | Multi-resolution wavelet | - |
dc.subject | Spatial dependence | - |
dc.title | Delineation of built-up areas from very high-resolution satellite imagery using multi-scale textures and spatial dependence | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.3390/rs10101596 | - |
dc.identifier.scopus | eid_2-s2.0-85055438788 | - |
dc.identifier.volume | 10 | - |
dc.identifier.issue | 10 | - |
dc.identifier.spage | article no. 1596 | - |
dc.identifier.epage | article no. 1596 | - |
dc.identifier.eissn | 2072-4292 | - |
dc.identifier.isi | WOS:000448555800096 | - |