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ADU-Net: Semantic segmentation of satellite imagery for land cover classification.
- Source :
-
Advances in Space Research . Sep2023, Vol. 72 Issue 5, p1780-1788. 9p. - Publication Year :
- 2023
-
Abstract
- Semantic Segmentation is an important problem in many vision related tasks. Land use and land cover classification involves semantic segmentation of satellite imagery and plays a vital role in many applications. In this paper, we propose an extended U-Net architecture with dense decoder connections and attention mechanism for pixel wise classification of satellite imagery named Attention Dense U-Net (ADU-Net). We further evaluate the effect of different upsampling strategies in the decoder part of the U-Net architecture. We evaluate our models on the Gaofen Image Dataset (GID) for landcover classification consisting of five classes: built-up, forest, farmland, meadow and water. The experiments on the GID dataset show better performance than the previous approaches. Our proposed architecture delivers more than 4% higher mIoU and F1-score than the baseline U-Net. Moreover, our proposed architecture achieves an F1-score of 87.21% and mIoU of 77.66% on the GID dataset. Our evaluations shows that data-dependent upsampling layer achieves higher accuracy than the Transposed Convolution, Pixel Shuffle and Bilinear upsampling layers. [ABSTRACT FROM AUTHOR]
- Subjects :
- *REMOTE-sensing images
*LAND cover
*ZONING
*LAND use
*CLASSIFICATION
Subjects
Details
- Language :
- English
- ISSN :
- 02731177
- Volume :
- 72
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- Advances in Space Research
- Publication Type :
- Academic Journal
- Accession number :
- 164964441
- Full Text :
- https://doi.org/10.1016/j.asr.2023.05.007