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.
ADU-Net: Semantic segmentation of satellite imagery for land cover classification
Advances in Space Research ; 72 , 5 ; 1780-1788
2023-05-04
9 pages
Article (Journal)
Electronic Resource
English
Incorporating Uncertainty in Land Cover Classification From Remote Sensing Imagery
British Library Conference Proceedings | 2000
|Segmentation and Classification in SAR Imagery
British Library Online Contents | 1997
|British Library Conference Proceedings | 2012
|