TOWARDS BETTER CLASSIFICATION OF LAND COVER AND LAND USE BASED ON CONVOLUTIONAL NEURAL NETWORKS

8 citations as recorded by crossref.

1. Class-Balanced Random Patch Training to Address Class Imbalance in Tiling-Based Farmland Classification Y. Bae & Y. Ban 10.3390/app15137056
2. [Retracted] A 3D‐2D Convolutional Neural Network and Transfer Learning for Hyperspectral Image Classification D. Nyabuga et al. 10.1155/2021/1759111
3. Appearance based deep domain adaptation for the classification of aerial images D. Wittich & F. Rottensteiner 10.1016/j.isprsjprs.2021.08.004
4. Multiscale Feature Reconstruction and Interclass Attention Weighting for Land Cover Classification Z. Zhan et al. 10.1109/JSTARS.2023.3342453
5. Impacts of urban forests and landscape characteristics on land surface temperature in two urban agglomeration areas of China W. Shen et al. 10.1016/j.scs.2023.104909
6. Weighted split-flow network auxiliary with hierarchical multitasking for urban land use classification of high-resolution remote sensing images G. He et al. 10.1080/01431161.2022.2143734
7. Assessing the effects of convolutional neural network architectural factors on model performance for remote sensing image classification: An in-depth investigation F. Chen & J. Tsou 10.1016/j.jag.2022.102865
8. DRSNet: Novel architecture for small patch and low-resolution remote sensing image scene classification F. Chen & J. Tsou 10.1016/j.jag.2021.102577