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Black and Odorous Water Detection of Remote Sensing Images Based on Improved Deep Learning.
- Source :
-
Canadian Journal of Remote Sensing . Feb2023, Vol. 49 Issue 1, p1-14. 14p. - Publication Year :
- 2023
-
Abstract
- Black and odorous water seriously affects the ecological balance of rivers and the health of people living nearby. Satellite remote sensing technology with its advantages of a large range, long-time series, low cost, and high efficiency, has provided a new area for water quality detection. Much archived remote sensing satellite data can be further processed and used as a data source for black and odorous water detection. In this paper, Gaofen-2 remote sensing data with a spatial resolution of 1 m is leveraged as the data source. To enrich the data source in the northern coastal zone of China, we have built a high-quality remote sensing dataset, called the remote sensing images for black and odorous water detection (RSBD) dataset, which is collected from the Gaofen-2 satellite in Yantai, China. In addition, we propose a network with an encoder-decoder discriminant structure for black and odorous water detection. In the network, an augmented attention module is designed to capture a more comprehensive semantic feature representation. Further, the median balancing loss function is adopted to solve the imbalance issues. Experimental results demonstrate that the network is superior to other state-of-the-art semantic segmentation methods on our dataset. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 07038992
- Volume :
- 49
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Canadian Journal of Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 174277086
- Full Text :
- https://doi.org/10.1080/07038992.2023.2237591