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| Format: | Preprint |
| Publié: |
2023
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| Accès en ligne: | https://arxiv.org/abs/2307.00314 |
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| _version_ | 1866912556762791936 |
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| author | Mukherjee, Jit |
| author_facet | Mukherjee, Jit |
| contents | Sand mining is a booming industry. The river sandbank is one of the primary sources of sand mining. Detection of potential river sandbank regions for sand mining directly impacts the economy, society, and environment. In the past, semi-supervised and supervised techniques have been used to detect mining regions including sand mining. A few techniques employ multi-modal analysis combining different modalities such as multi-spectral imaging, synthetic aperture radar (\emph{SAR}) imaging, aerial images, and point cloud data. However, the distinguishing spectral characteristics of river sandbank regions are yet to be fully explored. This paper provides a novel method to detect river sandbank regions for sand mining using multi-spectral images without any labeled data over the seasons. Association with a river stream and the abundance of minerals are the most prominent features of such a region. The proposed work uses these distinguishing features to determine the spectral signature of a river sandbank region, which is robust to other high mineral abundance regions. It follows a two-step approach, where first, potential high mineral regions are detected and next, they are segregated using the presence of a river stream. The proposed technique provides average accuracy, precision, and recall of 90.75%, 85.47%, and 73.5%, respectively over the seasons from Landsat 8 images without using any labeled dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2307_00314 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Detection of River Sandbank for Sand Mining with the Presence of Other High Mineral Content Regions Using Multi-spectral Images Mukherjee, Jit Computer Vision and Pattern Recognition Sand mining is a booming industry. The river sandbank is one of the primary sources of sand mining. Detection of potential river sandbank regions for sand mining directly impacts the economy, society, and environment. In the past, semi-supervised and supervised techniques have been used to detect mining regions including sand mining. A few techniques employ multi-modal analysis combining different modalities such as multi-spectral imaging, synthetic aperture radar (\emph{SAR}) imaging, aerial images, and point cloud data. However, the distinguishing spectral characteristics of river sandbank regions are yet to be fully explored. This paper provides a novel method to detect river sandbank regions for sand mining using multi-spectral images without any labeled data over the seasons. Association with a river stream and the abundance of minerals are the most prominent features of such a region. The proposed work uses these distinguishing features to determine the spectral signature of a river sandbank region, which is robust to other high mineral abundance regions. It follows a two-step approach, where first, potential high mineral regions are detected and next, they are segregated using the presence of a river stream. The proposed technique provides average accuracy, precision, and recall of 90.75%, 85.47%, and 73.5%, respectively over the seasons from Landsat 8 images without using any labeled dataset. |
| title | Detection of River Sandbank for Sand Mining with the Presence of Other High Mineral Content Regions Using Multi-spectral Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2307.00314 |