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Land Cover Classification from Remote Sensing Images Based on Multi-Scale Fully Convolutional Network

arXiv 2008.00168 ↗First posted 1 Aug 2020cs.CV cs.AI eess.IVcited by 11

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Abstract (arXiv)

In this paper, a Multi-Scale Fully Convolutional Network (MSFCN) with multi-scale convolutional kernel is proposed to exploit discriminative representations from two-dimensional (2D) satellite images.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Aug 2020.

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Citing papers per year: 2021: 4; 2022: 0; 2023: 2; 2024: 4; 2025: 1.

11 papers.

  1. 2021UNetFormer: A UNet-like Transformer for Efficient Semantic Segmentation of Remote Sensing Urban Scene Imagerycited by 166
  2. 2021A Novel Transformer Based Semantic Segmentation Scheme for Fine-Resolution Remote Sensing Imagescited by 55
  3. 2023SAM-Assisted Remote Sensing Imagery Semantic Segmentation with Object and Boundary Constraintscited by 35
  4. 2021Building extraction with vision transformercited by 22
  5. 2024SFFNet: A Wavelet-Based Spatial and Frequency Domain Fusion Network for Remote Sensing Segmentationcited by 16
  6. 2025Prototype-Driven Structure Synergy Network for Remote Sensing Images Segmentationcited by 11
  7. 2024ASANet: Asymmetric Semantic Aligning Network for RGB and SAR image land cover classificationcited by 10
  8. 2024Remote Sensing Image Segmentation Using Vision Mamba and Multi-Scale Multi-Frequency Feature Fusioncited by 6
  9. 2021A2-FPN for Semantic Segmentation of Fine-Resolution Remotely Sensed Imagescited by 0
  10. 2023Boosting Multimodal Remote Sensing Image Classification with Transformer-based Heterogeneously Salient Graph Representationcited by 0
  11. 2024Deep Learning Meets OBIA: Tasks, Challenges, Strategies, and Perspectivescited by 0