INVESTIGATING THE POTENTIAL OF DEEP NEURAL NETWORKS FOR LARGE-SCALE CLASSIFICATION OF VERY HIGH RESOLUTION SATELLITE IMAGES

11 citations as recorded by crossref.

1. A Metric for Evaluating the Geometric Quality of Land Cover Maps Generated with Contextual Features from High-Dimensional Satellite Image Time Series without Dense Reference Data D. Derksen et al. https://doi.org/10.3390/rs11161929
2. Land Cover Maps Production with High Resolution Satellite Image Time Series and Convolutional Neural Networks: Adaptations and Limits for Operational Systems A. Stoian et al. https://doi.org/10.3390/rs11171986
3. Geometry Aware Evaluation of Handcrafted Superpixel-Based Features and Convolutional Neural Networks for Land Cover Mapping Using Satellite Imagery D. Derksen et al. https://doi.org/10.3390/rs12030513
4. Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series C. Pelletier et al. https://doi.org/10.3390/rs11050523
5. Research on hierarchical and sub‐area network control technology of LEO giant constellation C. He et al. https://doi.org/10.1049/cmu2.12725
6. Deep convolutional neural networks in hyperspectral remote sensing data processing L. Bass et al. https://doi.org/10.20948/prepr-2018-282
7. Land cover dynamic change in the Napahai Basin using the optimized random forest model C. Bai et al. https://doi.org/10.1117/1.JRS.13.044518
8. Multispectral Data Analysis for Semantic Assessment—A SNAP Framework for Sentinel-2 Use Case Scenarios A. Grivei et al. https://doi.org/10.1109/JSTARS.2020.3013091
9. Temporal convolutional neural network for land use and land cover classification using satellite images time series T. Ló et al. https://doi.org/10.1007/s12517-023-11688-4
10. Identifying building locations in the wildland–urban interface before and after fires with convolutional neural networks N. Kasraee et al. https://doi.org/10.1071/WF22181
11. Implications of landscape configuration on understory forage productivity: a remote sensing assessment of native forests openings F. Trinco et al. https://doi.org/10.1007/s10457-021-00676-w