DEEP LEARNING FOR REMOTE SENSING IMAGE CLASSIFICATION FOR AGRICULTURE APPLICATIONS

14 citations as recorded by crossref.

1. INTELLIGENT ACQUISITION METHOD OF HERBACEOUS FLOWERS IMAGE BASED ON THEME CRAWLER, DEEP LEARNING AND GAME THEORY Z. Xie et al. 10.52013/2658-7556-66-4-12
2. APPLICATION OF FUZZY METRICS IN CLUSTERING PROBLEMS OF AGRICULTURAL CROP VARIETIES A. Stamenković et al. 10.59267/ekoPolj2401121S
3. Classification of Citrus Canker and Black Spot Diseases using a Deep Learning based Approach G. Adeem et al. 10.21015/vtess.v15i3.976
4. Unsupervised remote sensing image thin cloud removal method based on contrastive learning Z. Tan et al. 10.1049/ipr2.13067
5. Satellite Video Remote Sensing for Flood Model Validation C. Masafu & R. Williams 10.1029/2023WR034545
6. Measurement of Fracture Networks in Rock Sample by X-Ray Tomography, Convolutional Filtering and Deep Learning A. Caputo et al. 10.3390/s25144409
7. A Comprehensive Review of Deep Learning-Based Anomaly Detection Methods for Precision Agriculture K. Gkountakos et al. 10.1109/ACCESS.2024.3522248
8. Leveraging Deep Learning in Remote Sensing: A Novel Approach for Agricultural Greenhouse Detection and Innovation Management V. Tomco et al. 10.26833/ijeg.1587264
9. A novel machine learning approach to classify the remote sensing optically images based on swarm intelligence Y. Xiong & T. Zhang 10.1007/s11082-023-04989-1
10. MASPP and MWASP: multi-head self-attention based modules for UNet network in melon spot segmentation K. Tran et al. 10.1007/s11694-024-02466-1
11. A Novel Enhanced VGG16 Model to Tackle Grapevine Leaves Diseases With Automatic Method S. Mousavi & G. Farahani 10.1109/ACCESS.2022.3215639
12. Deep Learning Based Mineral Image Classification Combined With Visual Attention Mechanism Y. Liu et al. 10.1109/ACCESS.2021.3095368
13. A comprehensive systematic review of deep learning methods for hyperspectral images classification P. Ranjan & A. Girdhar 10.1080/01431161.2022.2133579
14. Agricultural Text Classification Method Based on Dynamic Fusion of Multiple Features Y. Li et al. 10.1109/ACCESS.2023.3253386