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Land Cover Image Classification

arXiv 2401.09607 ↗First posted 17 Jan 2024cs.CV cs.LG eess.IVcited by 0

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Abstract (arXiv)

Land Cover (LC) image classification has become increasingly significant in understanding environmental changes, urban planning, and disaster management. However, traditional LC methods are often labor-intensive and prone to human error. This paper explores state-of-the-art deep learning models for enhanced accuracy and efficiency in LC analysis. We compare convolutional neural networks (CNN) against transformer-based methods, showcasing their applications and advantages in LC studies. We used EuroSAT, a patch-based LC classification data set based on Sentinel-2 satellite images and achieved state-of-the-art results using current transformer models.

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16 of 45 references.

References · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
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  9. 2019Searching for MobileNetV3cited by 1,994
  10. 2017EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classificationcited by 1,496
  11. 2021EfficientNetV2: Smaller Models and Faster Trainingcited by 1,135
  12. 2022MaxViT: Multi-Axis Vision Transformercited by 476
4 more
  1. 2021SpectralFormer: Rethinking Hyperspectral Image Classification with Transformerscited by 169
  2. 2018Land cover mapping at very high resolution with rotation equivariant CNNs: towards small yet accurate modelscited by 29
  3. 2020Few-Shot Hyperspectral Image Classification With Unknown Classes Using Multitask Deep Learningcited by 13
  4. 2020Hyperspectral Anomaly Change Detection Based on Auto-encodercited by 11

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