Consensus Techniques for Unsupervised Binary Change Detection Using Multi-Scale Segmentation Detectors for Land Cover Vegetation Images
Change detection in very-high-spatial-resolution (VHR) remote sensing images is a very challenging area with applicability in many problems ranging from damage assessment to land management and environmental monitoring. In this study, we investigated the change detection problem associated with anal...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/11/2889 |
| _version_ | 1797596790118154240 |
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| author | F. Javier Cardama Dora B. Heras Francisco Argüello |
| author_facet | F. Javier Cardama Dora B. Heras Francisco Argüello |
| author_sort | F. Javier Cardama |
| collection | DOAJ |
| description | Change detection in very-high-spatial-resolution (VHR) remote sensing images is a very challenging area with applicability in many problems ranging from damage assessment to land management and environmental monitoring. In this study, we investigated the change detection problem associated with analysing the vegetation corresponding to crops and natural ecosystems over VHR multispectral and hyperspectral images obtained by sensors onboard drones or satellites. The challenge of applying change detection methods to these images is the similar spectral signatures of the vegetation elements in the image. To solve this issue, a consensus multi-scale binary change detection technique based on the extraction of object-based features was developed. With the objective of capturing changes at different granularity levels taking advantage of the high spatial resolution of the VHR images and, as the segmentation operation is not well defined, we propose to use several detectors based on different segmentation algorithms, each applied at different scales. As the changes in vegetation also present high variability depending on capture conditions such as illumination, the use of the CVA-SAM applied at the segment level instead of at the pixel level is also proposed. The results revealed the effectiveness of the proposed approach for identifying changes over land cover vegetation images with different types of changes and different spatial and spectral resolutions. |
| first_indexed | 2024-03-11T02:58:00Z |
| format | Article |
| id | doaj.art-c23ced0ace8c4529b2c784e84670f409 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T02:58:00Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c23ced0ace8c4529b2c784e84670f4092023-11-18T08:30:06ZengMDPI AGRemote Sensing2072-42922023-06-011511288910.3390/rs15112889Consensus Techniques for Unsupervised Binary Change Detection Using Multi-Scale Segmentation Detectors for Land Cover Vegetation ImagesF. Javier Cardama0Dora B. Heras1Francisco Argüello2Centro Singular de Investigación en Tecnologías Inteligentes, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, SpainCentro Singular de Investigación en Tecnologías Inteligentes, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, SpainDepartamento de Electrónica y Computación, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, SpainChange detection in very-high-spatial-resolution (VHR) remote sensing images is a very challenging area with applicability in many problems ranging from damage assessment to land management and environmental monitoring. In this study, we investigated the change detection problem associated with analysing the vegetation corresponding to crops and natural ecosystems over VHR multispectral and hyperspectral images obtained by sensors onboard drones or satellites. The challenge of applying change detection methods to these images is the similar spectral signatures of the vegetation elements in the image. To solve this issue, a consensus multi-scale binary change detection technique based on the extraction of object-based features was developed. With the objective of capturing changes at different granularity levels taking advantage of the high spatial resolution of the VHR images and, as the segmentation operation is not well defined, we propose to use several detectors based on different segmentation algorithms, each applied at different scales. As the changes in vegetation also present high variability depending on capture conditions such as illumination, the use of the CVA-SAM applied at the segment level instead of at the pixel level is also proposed. The results revealed the effectiveness of the proposed approach for identifying changes over land cover vegetation images with different types of changes and different spatial and spectral resolutions.https://www.mdpi.com/2072-4292/15/11/2889multispectralchange detectionchange vector analysissuperpixel segmentationmulti-scaledata fusion |
| spellingShingle | F. Javier Cardama Dora B. Heras Francisco Argüello Consensus Techniques for Unsupervised Binary Change Detection Using Multi-Scale Segmentation Detectors for Land Cover Vegetation Images Remote Sensing multispectral change detection change vector analysis superpixel segmentation multi-scale data fusion |
| title | Consensus Techniques for Unsupervised Binary Change Detection Using Multi-Scale Segmentation Detectors for Land Cover Vegetation Images |
| title_full | Consensus Techniques for Unsupervised Binary Change Detection Using Multi-Scale Segmentation Detectors for Land Cover Vegetation Images |
| title_fullStr | Consensus Techniques for Unsupervised Binary Change Detection Using Multi-Scale Segmentation Detectors for Land Cover Vegetation Images |
| title_full_unstemmed | Consensus Techniques for Unsupervised Binary Change Detection Using Multi-Scale Segmentation Detectors for Land Cover Vegetation Images |
| title_short | Consensus Techniques for Unsupervised Binary Change Detection Using Multi-Scale Segmentation Detectors for Land Cover Vegetation Images |
| title_sort | consensus techniques for unsupervised binary change detection using multi scale segmentation detectors for land cover vegetation images |
| topic | multispectral change detection change vector analysis superpixel segmentation multi-scale data fusion |
| url | https://www.mdpi.com/2072-4292/15/11/2889 |
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