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Witharana, C., Bhuiyan, M.A.E., Liljedahl, A.K., Kanevskiy, M., Epstein, H.E., Jones, B.M., Daanen, R., Griffin, C.G., Kent, K. and Jones, M.K.W. (2020) Understanding the Synergies of Deep Learning and Data Fusion of Multispectral and Panchromatic High Resolution Commercial Satellite Imagery for Automated Ice-Wedge Polygon Detection. ISPRS Journal of Photogrammetry and Remote Sensing, 170, 174-191.
https://doi.org/10.1016/j.isprsjprs.2020.10.010

has been cited by the following article:

• TITLE: New Fusion Approach of Spatial and Channel Attention for Semantic Segmentation of Very High Spatial Resolution Remote Sensing Images

  AUTHORS: Armand Kodjo Atiampo, Gokou Hervé Fabrice Diédié

  KEYWORDS: Spatial-Channel Attention, Super-Token Segmentation, Self-Attention, Vision Transformer

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.14 No.2, February 9, 2024

  ABSTRACT: The semantic segmentation of very high spatial resolution remote sensing images is difficult due to the complexity of interpreting the interactions between the objects in the scene. Indeed, effective segmentation requires considering spatial local context and long-term dependencies. To address this problem, the proposed approach is inspired by the MAC-UNet network which is an extension of U-Net, densely connected combined with channel attention. The advantages of this solution are as follows: 1) The new model introduces a new attention called propagate attention to build an attention-based encoder. 2) The fusion of multi-scale information is achieved by a weighted linear combination of the attentions whose coefficients are learned during the training phase. 3) Introducing in the decoder, the Spatial-Channel-Global-Local block which is an attention layer that uniquely combines channel attention and spatial attention locally and globally. The performances of the model are evaluated on 2 datasets WHDLD and DLRSD and show results of mean intersection over union (mIoU) index in progress between 1.54% and 10.47% for DLRSD and between 1.04% and 4.37% for WHDLD compared with the most efficient algorithms with attention mechanisms like MAU-Net and transformers like TMNet.