Learning-Based Sub-Pixel Change Detection Using Coarse Resolution Satellite Imagery
Moderate Resolution Imaging Spectroradiometer (MODIS) data are effective and efficient for monitoring urban dynamics such as urban cover change and thermal anomalies, but the spatial resolution provided by MODIS data is 500 m (for most of its shorter spectral bands), which results in difficulty in d...
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/9/7/709 |
| _version_ | 1819289300150255616 |
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| author | Yong Xu Lin Lin Deyu Meng |
| author_facet | Yong Xu Lin Lin Deyu Meng |
| author_sort | Yong Xu |
| collection | DOAJ |
| description | Moderate Resolution Imaging Spectroradiometer (MODIS) data are effective and efficient for monitoring urban dynamics such as urban cover change and thermal anomalies, but the spatial resolution provided by MODIS data is 500 m (for most of its shorter spectral bands), which results in difficulty in detecting subtle spatial variations within a coarse pixel—especially for a fast-growing city. Given that the historical land use/cover products and satellite data at finer resolution are valuable to reflect the urban dynamics with more spatial details, finer spatial resolution images, as well as land cover products at previous times, are exploited in this study to improve the change detection capability of coarse resolution satellite data. The proposed approach involves two main steps. First, pairs of coarse and finer resolution satellite data at previous times are learned and then applied to generate synthetic satellite data with finer spatial resolution from coarse resolution satellite data. Second, a land cover map was produced at a finer spatial resolution and adjusted with the obtained synthetic satellite data and prior land cover maps. The approach was tested for generating finer resolution synthetic Landsat images using MODIS data from the Guangzhou study area. The finer resolution Landsat-like data were then applied to detect land cover changes with more spatial details. Test results show that the change detection accuracy using the proposed approach with the synthetic Landsat data is much better than the results using the original MODIS data or conventional spatial and temporal fusion-based approaches. The proposed approach is beneficial for detecting subtle urban land cover changes with more spatial details when multitemporal coarse satellite data are available. |
| first_indexed | 2024-12-24T03:04:39Z |
| format | Article |
| id | doaj.art-f9aef7accb3549d6857875769ff115ab |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-24T03:04:39Z |
| publishDate | 2017-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-f9aef7accb3549d6857875769ff115ab2022-12-21T17:18:04ZengMDPI AGRemote Sensing2072-42922017-07-019770910.3390/rs9070709rs9070709Learning-Based Sub-Pixel Change Detection Using Coarse Resolution Satellite ImageryYong Xu0Lin Lin1Deyu Meng2Institute of Future Cities, The Chinese University of Hong Kong, Hong Kong, ChinaSchool of Mathematics and Statistics and Ministry of Education Key Lab of Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an 710000, ChinaSchool of Mathematics and Statistics and Ministry of Education Key Lab of Intelligent Networks and Network Security, Xi’an Jiaotong University, Xi’an 710000, ChinaModerate Resolution Imaging Spectroradiometer (MODIS) data are effective and efficient for monitoring urban dynamics such as urban cover change and thermal anomalies, but the spatial resolution provided by MODIS data is 500 m (for most of its shorter spectral bands), which results in difficulty in detecting subtle spatial variations within a coarse pixel—especially for a fast-growing city. Given that the historical land use/cover products and satellite data at finer resolution are valuable to reflect the urban dynamics with more spatial details, finer spatial resolution images, as well as land cover products at previous times, are exploited in this study to improve the change detection capability of coarse resolution satellite data. The proposed approach involves two main steps. First, pairs of coarse and finer resolution satellite data at previous times are learned and then applied to generate synthetic satellite data with finer spatial resolution from coarse resolution satellite data. Second, a land cover map was produced at a finer spatial resolution and adjusted with the obtained synthetic satellite data and prior land cover maps. The approach was tested for generating finer resolution synthetic Landsat images using MODIS data from the Guangzhou study area. The finer resolution Landsat-like data were then applied to detect land cover changes with more spatial details. Test results show that the change detection accuracy using the proposed approach with the synthetic Landsat data is much better than the results using the original MODIS data or conventional spatial and temporal fusion-based approaches. The proposed approach is beneficial for detecting subtle urban land cover changes with more spatial details when multitemporal coarse satellite data are available.https://www.mdpi.com/2072-4292/9/7/709land cover changedownscalingsub-pixel change detectionmachine learningMODISLandsat |
| spellingShingle | Yong Xu Lin Lin Deyu Meng Learning-Based Sub-Pixel Change Detection Using Coarse Resolution Satellite Imagery Remote Sensing land cover change downscaling sub-pixel change detection machine learning MODIS Landsat |
| title | Learning-Based Sub-Pixel Change Detection Using Coarse Resolution Satellite Imagery |
| title_full | Learning-Based Sub-Pixel Change Detection Using Coarse Resolution Satellite Imagery |
| title_fullStr | Learning-Based Sub-Pixel Change Detection Using Coarse Resolution Satellite Imagery |
| title_full_unstemmed | Learning-Based Sub-Pixel Change Detection Using Coarse Resolution Satellite Imagery |
| title_short | Learning-Based Sub-Pixel Change Detection Using Coarse Resolution Satellite Imagery |
| title_sort | learning based sub pixel change detection using coarse resolution satellite imagery |
| topic | land cover change downscaling sub-pixel change detection machine learning MODIS Landsat |
| url | https://www.mdpi.com/2072-4292/9/7/709 |
| work_keys_str_mv | AT yongxu learningbasedsubpixelchangedetectionusingcoarseresolutionsatelliteimagery AT linlin learningbasedsubpixelchangedetectionusingcoarseresolutionsatelliteimagery AT deyumeng learningbasedsubpixelchangedetectionusingcoarseresolutionsatelliteimagery |